| 1. |
- Almlöf, Erik, 1985-, et al.
(författare)
-
Who continued travelling by public transport during COVID-19? : Socioeconomic factors explaining travel behaviour in Stockholm 2020 based on smart card data
- 2021
-
Ingår i: European Transport Research Review. - : Springer Nature. - 1867-0717 .- 1866-8887. ; 13:1
-
Forskningsöversikt (refereegranskat)abstract
- Introduction The COVID-19 pandemic has changed travel behaviour and reduced the use of public transport throughout the world, but the reduction has not been uniform. In this study we analyse the propensity to stop travelling by public transport during COVID-19 for the holders of 1.8 million smart cards in Stockholm, Sweden, for the spring and autumn of 2020. We suggest two binomial logit models for explaining the change in travel pattern, linking socioeconomic data per area and travel data with the probability to stop travelling. Modelled variables The first model investigates the impact of the socioeconomic factors: age; income; education level; gender; housing type; population density; country of origin; and employment level. The results show that decreases in public transport use are linked to all these factors. The second model groups the investigated areas into five distinct clusters based on the socioeconomic data, showing the impacts for different socioeconomic groups. During the autumn the differences between the groups diminished, and especially Cluster 1 (with the lowest education levels, lowest income and highest share of immigrants) reduced their public transport use to a similar level as the more affluent clusters. Results The results show that socioeconomic status affect the change in behaviour during the pandemic and that exposure to the virus is determined by citizens' socioeconomic class. Furthermore, the results can guide policy into tailoring public transport supply to where the need is, instead of assuming that e.g. crowding is equally distributed within the public transport system in the event of a pandemic.
|
|
| 2. |
- Almlöf, Erik, 1985-, et al.
(författare)
-
Who is still travelling by public transport during COVID-19? : Socioeconomic factors explaining travel behaviour in Stockholm based on smart card data
- 2021
-
Konferensbidrag (refereegranskat)abstract
- The COVID-19 pandemic has changed travel behaviour and reduced the use of public transport throughout the world, but the reduction has not been uniform. In this study we analyse the propensity to stop travelling by public transport during COVID-19 for the holders of 1.8 million smart cards in Stockholm, Sweden. We suggest two models for explaining the change in travel pattern, linking socioeconomic data with the probability to stop travelling. We find that education level, income and age are strong predictors, but that workplace type also substantially affect the propensity of public transport travel. Furthermore, we use clustering to divide the population into five separate social groups, serving as a more intuitive understanding of how the pandemic has affected different citizens’ propensity to use public transport. The results can guide policy makers on how to better tail e.g. bus supply to local demand, either through an increased understanding of differences based on the results or by further incorporating the results into a transport simulation models.
|
|
| 3. |
- Burghout, Wilco, et al.
(författare)
-
Multimodal Traffic Management : Project Report
- 2024
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Nya system för att kombinera transportsätt, till exempel Mobility as a Service (MaaS), ger nya möjligheter för trafikanter att växla mellan olika färdmedel. Samtidigt ger stora mängder data från såväl kollektivtrafiknätet som vägtrafiknätet samt multimodala data från mobilnäten i kombination med nya metoder för att uppskatta resmönster uppdelat på färdmedel möjligheter till en helt ny förståelse av multimodala resmönster i en stad. Att förstå hur multimodala resmönster utvecklas över tid ger nya möjligheter att utveckla effektiva verktyg för multimodal trafikledning.Det övergripande målet med projektet är att möjliggöra förbättrad tillgänglighet i transportsystemen genom effektivare trafikledning. Mer specifikt syftar projektet till att utveckla nya metoder för att uppskatta multimodal efterfrågan samt färdmedelsval och ruttval för multimodal trafikledning. Vidare har potentiella effekter av multimodal trafikledning analyserats.Projektet omfattar en litteraturstudie för analys av möjligheter och utmaningar med multimodal trafikledning. En explorativ analys baserad på oövervakat lärande har utförts för att identifiera typiska nätverksövergripande mobilitetsmönster. Val av rutt och färdmedel har predikterats med hjälp av statistiska modeller. Ett multimodalt dataset för fem veckor i Stockholm med storskalig mobilitetsdata för vägnätet och biljettdata för kollektivtrafiknätet har sammanställs för den explorativa analysen samt utvärderingen av rutt- och transportsättsmodellerna i samband med trafikledning.Baserat på litteraturstudien kan vi dra slutsatsen att koordinerad ledning av väg och kollektivtrafik har potential att minska trängseln och säkerställa effektiv förflyttning av resenärer i ett storstadsområde. Det finns flera motiv för multimodal trafikledning, där de viktigaste är potentiellt ökad efterfrågan för kollektivtrafik, förbättrad robusthet för transportsystemet och bättre prioritering av trafikledningsåtgärder. De största utmaningarna är samarbete mellan intressenter, informationsdelning och datafusion.Resultaten av den explorativa analysen baserad på oövervakad inlärning indikerar att klustring för att ta fram typdagar kan vara användbart vid scenarioutvärdering, men också fungera som input till korttidsprediktion, vilket ger en enkel och robust predikteringsmetod för länkflöden med ett MAPE-prediktionsfel på 10-15 %.Ruttvalsanalysen visar att en modell baserad på en ruttuppsättning med genererade rutter är mer responsiv för restidsförändringar än en modell baserad på endast observerade rutter, vilket är användbart för att förutspå effekten av olika trafikledningsåtgärder. En ruttvalsmodell med enbart restid är en vanlig förenkling att använda för att prediktera ruttval, men resultatet i denna studie visar att inkludering av fler attribut avsevärt förbättrar modellernas prestanda.Analysen av nätverksövergripande multimodala data för 5 veckor i Stockholm indikerar att det är möjligt att uppskatta hur transportsättsandelen mellan kollektivtrafik och andra transportslag varierar i tid och rum. En bättre förståelse för spatiotemporal variation av färdmedelsvalet är en viktig input till förbättrat beslutsstöd i multimodal trafikledning.
|
|
| 4. |
|
|
| 5. |
- Cats, Oded, 1983-, et al.
(författare)
-
How fair is the fare? Estimating travel patterns and the impacts of fare schemes for different user groups in Stockholm based on smartcard data : Final report for Trafik och Region 2018 SLL-KTH research project
- 2019
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- There is a rapid increase in the deployment, acquisition and analysis of automated fare collection (AFC) systems, enabling a profound change in the ability to analyze high-volume data that relate to observed passenger travel behavior and recurrent patterns. The analysis of such passively collected data offers direct access to a continuous flow of observed passenger behavior at a large scale, saving expensive data collection efforts. For a review of the spectrum of applications – from strategic demand estimation to operational service performance measurements.The FairAccess project leverages on the availability of Access-kort data for the vast majority of trips performed in Stockholm County. The overarching goal of this project is to develop means to analyse empirically the impacts of policy/planning measures based on disaggregate passively collected smart card data. This involves a series of analysis and modelling challenges. We develop and apply a series algorithms to infer of tap-out locations, infer vehicles and travel times, and infer transfers to that journeys can be composed. Tap-in records have been matched with corresponding inferred tap-out locations and time stamps for about 80% of all records. Thereafter, we construct time-dependent origin-destination matrices for which segmentations can be performed with respect to geographical and user product features.We demonstrate the approach and algorithms developed by performing a before-after analysis of the fare scheme change from zone-based to flat fares. We analyse changes in travel patterns and derive price elasticities for distinctive market segments. The introduced fare policy delivered the desirable result of an increased ridership through improved convenience of the single-use products. Nevertheless, the significance of the service convenience component was underestimated, which resulted in the price adjustments being not in line with the mobility effects.The planning and development of the Stockholm public transport system must rely on the best empirical foundations available to support evidence-based decision-making and make the right priorities. To this end, the development and analysis performed in the FairAccess project lay a necessary foundation for further methodological developments and analyses such as on-board crowding evaluation, demand forecasting and identifying user groups.
|
|
| 6. |
- Cats, Oded, 1983-, et al.
(författare)
-
Unravelling Mobility Patterns using Longitudinal Smart Card Data : Final report for Trafik och Region 2019SLL-KTH research project
- 2021
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- BackgroundThis project followed-up on a project called FairAccess which was granted in Trafik och Region 2018.In FairAccess, we processed Access card data and performed a sequence of inferences to derive timedependent origin-destination matrices for the entire Region Stockholm system. Tap-in records werematched with corresponding inferred tap-out locations and time stamps for about 80% of all records.Moreover, we implemented an algorithm to generate a journey database based on our transferinference method. We used the outputs of this process to evaluate the impacts of the fare schemechange (i.e. from zone-based to flat fare) on different user profiles. Access card products and zonalattributes were used for analysing policy impacts on different market segments.The “Unravelling Mobility Patterns using Longitudinal Smart Card Data” project was granted on May27, 2020 and the contract was signed on July 17, 2020. In this project, we capitalise on the capabilitiesof the inferences performed in previous work to conduct a series of market segmentation andadvanced data analytics to empirically analysis demand patterns for public transport in the StockholmCounty. The growing travel demand in Stockholm County is accompanied by an increased diversity ofsub-centres within the region as well as in individual travel patterns. It is thus increasingly importantto understand how demand patterns evolve over time, what the key market segments are and howdifferent users are affected by changes in service provision. The latter is studied in the contact of theopening of the Citybanan project.As stated in the SLL Research and Innovation Plan, the development of transport solutions for theStockholm region requires new knowledge regarding travellers’ needs and preferences, and theimpacts for different types of travellers.
|
|
| 7. |
- Cebecauer, Matej, et al.
(författare)
-
3D Speed Maps and Mean Observations Vectors for Short-Term Urban Traffic Prediction
- 2019
-
Ingår i: TRB Annual Meeting Online. - Washington DC, US. ; , s. 1-20, s. 1-20
-
Konferensbidrag (refereegranskat)abstract
- City-wide travel time prediction in real-time is an important enabler for efficient use of the road network. It can be used in traveler information to enable more efficient routing of individual vehicles as well as decision support for traffic management applications such as directed information campaigns or incident management. 3D speed maps have been shown to be a promising methodology for revealing day-to-day regularities of city-level travel times and possibly also for short-term prediction. In this paper, we aim to further evaluate and benchmark the use of 3D speed maps for short-term travel time prediction and to enable scenario-based evaluation of traffic management actions we also evaluate the framework for traffic flow prediction. The 3D speed map methodology is adapted to short-term prediction and benchmarked against historical mean as well as against Probabilistic Principal Component Analysis (PPCA). The benchmarking and analysis are made using one year of travel time and traffic flow data for the city of Stockholm, Sweden. The result of the case study shows very promising results of the 3D speed map methodology for short-term prediction of both travel times and traffic flows. The modified version of the 3D speed map prediction outperforms the historical mean prediction as well as the PPCA method. Further work includes an extended evaluation of the method for different conditions in terms of underlying sensor infrastructure, preprocessing and spatio-temporal aggregation as well as benchmarking against other prediction methods.
|
|
| 8. |
- Cebecauer, Matej, et al.
(författare)
-
A versatile adaptive aggregation framework for spatially large discrete location-allocation problems
- 2017
-
Ingår i: Computers & industrial engineering. - : Elsevier. - 0360-8352 .- 1879-0550. ; 111, s. 364-380
-
Tidskriftsartikel (refereegranskat)abstract
- We propose a versatile concept of the adaptive aggregation framework for the facility location problems that keeps the problem size in reasonable limits. Most location-allocation problems are known to be NP-hard. Thus, if a problem reaches the critical size, the computation exceeds reasonable time limits, or all computer memory is consumed. Aggregation is a tool that allows for transforming problems into smaller sizes. Usually, it is used only in the data preparation phase, and it leads to the loss of optimality due to aggregation errors. This is particularly remarkable when solving problems with a large number of demand points. The proposed framework embeds the aggregation into the solving process and it iteratively adjusts the aggregation level to the high quality solutions. To explore its versatility, we apply it to the p-median and to the lexicographic minimax problems that lead to structurally different patterns of located facilities. To evaluate the optimality errors, we use benchmarks which can be computed exactly, and to explore the limits of our approach, we study benchmarks reaching 670,000 demand points. Numerical experiments reveal that the adaptive aggregation framework performs well across a large range of problem sizes and is able to provide solutions of higher quality than the state-of-the-art exact methods when applied to the aggregated problem.
|
|
| 9. |
- Cebecauer, Matej
(författare)
-
Enhancing Short-Term Traffic Prediction for Large-Scale Transport Networks by Spatio-Temporal Clustering
- 2021
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Congestion in large cities is responsible for extra travel time, noise, air pollution, CO2 emissions, and more. Transport is one of the main recognized contributors to global warming and climate change, which is getting increasing attention from authorities and societies around the world. Better utilization of existing resources by Intelligent Transport Systems (ITS) and digital technologies are recognized by the European Commission as technologies with enormous potential to lower the negative impacts associated with high traffic volumes in urban areas.The main focus of this work is on short-term traffic prediction, which is an essential tool in ITS. In combination with providing information, it enables proactive decisions to decrease severity of congestion that occurs regularly or is caused by incidents. The main contribution of this work is to develop a methodological framework and prove its enhancing effects on short-term prediction in the context of large-scale transport networks. It is expected to contribute to more robust and accurate predictions of ITS in traffic management centers.Traffic patterns in large-scale networks, including urban streets, can be heterogeneous during the day and from day-to-day. This work investigates spatio-temporal clustering of heterogeneous data sets to smaller, more homogeneous data sub-sets. This is expected to produce more robust, accurate, scalable, and cost-effective prediction models. This thesis is the collection of five papers that contribute to enhancing short-term traffic prediction in this context. The clustering is recognized to boost prediction performance in Papers II, III, IV, and V. Paper II considers network partitioning and the last three papers study day clustering. The prediction models used across included papers are naive historical mean prediction models and more advanced prediction models such as probabilistic principal component analysis (PPCA) and exponential smoothing. Paper I considers and facilitates floating car data (FCD) as a cost-effective opportunistic source of speed and travel time data with extensive network coverage.Common practice in determining the number of clusters is to rely on internal evaluation indices, and these are very efficient but isolated from application. Paper IV tests this practice by also considering performance in short-term prediction application. Our results show that relying on these indices can lead to a loss of prediction accuracy of about 20% depending on the considered prediction model. Dimensionality reduction has a minimal effect on the resulting prediction performance, but clustering needs 20 times less computational time and only 0.1% of the original information.Finally, in Paper V, we look at similarities of representative day clusters recognized by speed and flows. Furthermore, the interchangeability of speed day-type centroids for flow when predicting speeds has proven to be robust, which is not a case for predicting flows by speed day-type centroids and observations.
|
|
| 10. |
- Cebecauer, Matej, et al.
(författare)
-
Generating Network-Wide Travel Diaries and OD Matrices Using Stockholm County Smartcard Data
- 2020
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Bakgrund: The public transport system in Stockholm extends across the greater Stockholm area, covering ca 6,500 km2 and 2.3 million inhabitants. The system includes 21 commuter train, metro, light rail and tram lines spanning ca 470 km, around 490 bus lines spanning ca 9,100 km, and a number of ferry lines (SLL 2016). The main ticketing system is the Access system, which uses electronic tickets that are loaded onto contactless cards. The system was introduced in limited scale in 2008 and the average number of ticket validations per day has since grown to 1.9 million in 2018. Trafikförvaltningen, Region Stockholm is collecting access smartcard data for several years. Just for year 2017 smartcard data consist of approximately 680 million tap-in records. The majority of tap-ins are recorded at metro gates (45%) and upon boarding buses (41%) while the remaining consists of commuter trains, trams, and ferries. Each card has a unique number, which allows it to be traced and construct the complete journeys and travel diaries. There is a big potential in using these data for different analysis, evaluation, and planning of public transport. We present the framework that enables processing of raw access data in fusion with AVL and network data to the network-wide travel diaries. Furthermore, the estimated OD matrices can be used for measuring the impacts of various interventions such as fare policy and service design changes. The inferred travel diaries also allow for extracting passenger loads for each vehicle trip segment across the network at the same resolution as the flow outputs of schedule-based transit assignment models.Metod: Tickets are validated upon access to stations or boarding of vehicles but not on egress or alighting. In other words, the Access system is “tap-in only”. We propose a method to estimate the alighting station in a multimodal public transport system, where tap-in transactions are observed in a complex network. Similar to previous literature it is assumed that the alighting occurs within a certain distance of the next transaction. Furthermore, vehicle and time inference using AVL data is performed. Trip elements are assessed individually resulting in individual travel diaries.Resultat och slutsats: The implemented inference algorithms and the derived travel diaries facilitate the construction of OD matrices that are essential input for services planning. The performance of the inferring algorithms is: for the alighting station: 87%; for travel time 70% using AVL data exclusively; considering all trips even without alighting station 86% of all journeys have inferred destination; from which 73% have travel time estimated.
|
|
| 11. |
|
|
| 12. |
- Cebecauer, Matej, et al.
(författare)
-
Integrated framework for real-time urban network travel time prediction on sparse probe data
- 2018
-
Ingår i: IET Intelligent Transport Systems. - : Institution of Engineering and Technology. - 1751-956X .- 1751-9578. ; 12:1, s. 66-74
-
Tidskriftsartikel (refereegranskat)abstract
- The study presents the methodology and system architecture of an integrated urban road network travel time prediction framework based on low-frequency probe vehicle data. Intended applications include real-time network traffic management, vehicle routing and information provision. The framework integrates methods for receiving a stream of probe vehicle data, map matching and path inference, link travel time estimation, calibration of prediction model parameters and network travel time prediction in real time. The system design satisfies three crucial aspects: computational efficiency of prediction, internal consistency between components and robustness against noisy and missing data. Prediction is based on a multivariate hybrid method of probabilistic principal component analysis, which captures global correlation patterns between links and time intervals, and local smoothing, which considers local correlations among neighbouring links. Computational experiments for the road network of Stockholm, Sweden and probe data from taxis show that the system provides high accuracy for both peak and off-peak traffic conditions. The computational efficiency of the framework makes it capable of real-time prediction for large-scale networks. For links with large speed variations between days, prediction significantly outperforms the historical mean. Furthermore, prediction is reliable also for links with high proportions of missing data.
|
|
| 13. |
- Cebecauer, Matej, et al.
(författare)
-
Integrating Demand Responsive Services into Public Transport Disruption Management
- 2021
-
Ingår i: IEEE Open Journal of Intelligent Transportation Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2687-7813. ; 2, s. 24-36
-
Tidskriftsartikel (refereegranskat)abstract
- High-capacity public transport services such as metro and commuter trains are efficient during normal operations but are vulnerable to disruptions. To manage disruptions, bridging buses are commonly called in to replace the rail-based service along the disrupted lines. These often take significant time to arrive and are costly to keep stand-by. Demand-responsive transport such as taxi can respond to demand almost immediately but is costly and must usually be arranged by the individual travelers. This study examines the integration and potential role of demand-responsive transport in disruption management. The analysis considers the impacts of limiting the serving area, varying the number of available vehicles, pursuing ride-sharing, as well as a system-of-systems approach with collaboration between taxis and bridging buses. Results of computational experiments on the case study of Stockholm, Sweden reveal that integration of demand-responsive transport in the disruption management can bring large positive benefits in terms of average and maximum waiting times for travelers. This is especially the case for strategies including ridesharing. It is also shown that appropriate trade-offs between desired waiting times and costs can be achieved by collaboration of both bridging buses and demand-responsive transport. Additionally, more robust public transport with increased reliability during disruptions can increase sustainability as more people may choose public transport instead of private cars.
|
|
| 14. |
- Cebecauer, Matej, et al.
(författare)
-
Large-scale test data set for location problems
- 2018
-
Ingår i: Data in Brief. - : Elsevier Inc.. - 2352-3409. ; 17, s. 267-274
-
Tidskriftsartikel (refereegranskat)abstract
- Designers of location algorithms share test data sets (benchmarks) to be able to compare performance of newly developed algorithms. In previous decades, the availability of locational data was limited. Big data has revolutionised the amount and detail of information available about human activities and the environment. It is expected that integration of big data into location analysis will increase the resolution and precision of input data. Consequently, the size of solved problems will significantly increase the demand on the development of algorithms that will be able to solve such problems. Accessibility of realistic large scale test data sets, with the number of demands points above 100,000, is very limited. The presented data set covers entire area of Slovakia and consists of the graph of the road network and almost 700,000 connected demand points. The population of 5.5 million inhabitants is allocated to the locations of demand points considering the residential population grid to estimate the size of the demand. The resolution of demand point locations is 100 m. With this article the test data is made publicly available to enable other researches to investigate their algorithms. The second area of its utilisation is the design of methods to eliminate aggregation errors that are usually present when considering location problems of such size. The data set is related to two research articles: “A Versatile Adaptive Aggregation Framework for Spatially Large Discrete Location-Allocation Problem” (Cebecauer and Buzna, 2017) [1] and “Effects of demand estimates on the evaluation and optimality of service centre locations” (Cebecauer et al., 2016) [2].
|
|
| 15. |
- Cebecauer, Matej, et al.
(författare)
-
Public transport disruption management by collaboration with demand responsive services
- 2020
-
Konferensbidrag (refereegranskat)abstract
- For large cities, public transport represents the backbone for commuters and thus plays a crucial role for society and for the economy. High-capacity public transport services such as metro and commuter trains are efficient during normal operations but are vulnerable to disruptions. Metro and commuter train disruptions can be handled in several ways. Very common are bridging buses that are called in to replace the rail-based service along the disrupted lines. These often take significant time to arrive and are costly to keep stand-by. Demand-responsive transport such as taxi can respond to demand almost immediately but is costly and must usually be arranged by the individual travelers. This study examines the integration and potential role of demand-responsive transport in disruption management. The analysis considers the impacts of limiting the serving area, varying the number of available vehicles, pursuing ridesharing, as well as a system-of-systems approach with collaboration between taxis and bridging buses. Results of computational experiments on the case study of Stockholm, Sweden reveal that integration of demand-responsive transport in the disruption management can bring large positive benefits in terms of average and maximum waiting times for travelers. This is especially the case for strategies including ridesharing. It is also shown that appropriate trade-offs between desired waiting times and costs can be achieved by collaboration of both bridging buses and demand-responsive transport. Additionally, it is expected that more robust public transport with increased reliability during disruptions can increase sustainability as more people may choose public transport instead of private cars.
|
|
| 16. |
- Cebecauer, Matej, et al.
(författare)
-
Real-time city-level traffic prediction in the context of Stockholm City
- 2019
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Background: The ongoing POST (Prediktions- och Scenariobaserad Trafikledning) project and the previous project Mobile Millennium Stockholm (MMS) provided tools and frameworks for real-time estimation and prediction of travel times on the city-level. City-level prediction of the traffic state as well as the traffic demand is important for both traveler information applications, such as online navigation, and traffic management applications, such as scenario evaluation of incident management strategies. However, city-level prediction is very challenging and requires efficient processing of large amounts of data. Here we present the recent research about effects of the clustering on the prediction performance and computational cost. Partitioning of the road network based on spatial and temporal attributes can potentially result in clusters that provide more robust and accurate prediction with reasonable bias-variance tradeoff. Methods: The effects of the clustering on the prediction performance are studied on the three case studies, representing different travel time sources in Stockholm city. First represent 15 MCS radars as the sources of travel times. Second 420 segments on the major roads around Stockholm with travel times estimated from the MCS radars. Third, travel times of 11,340 links processed from GPS data of 1,500 taxis operating in Stockholm. With the computational experiments, we studied different clustering approaches based on the day classification, functional classes, spatial locations and temporal attributes, and how they can effect the prediction performance and computational cost.Results: reveal that partitioning can significantly improve the prediction accuracy and rapidly decrease the computational cost and time.
|
|
| 17. |
- Cebecauer, Matej, et al.
(författare)
-
Revealing representative day-types in transport networks using traffic data clustering
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Recognition of spatio-temporal traffic patterns at the network-wide level plays an important role in data-driven intelligent transport systems (ITS) and is a basis for applications such as short-term prediction and scenario-based traffic management. Common practice in the transport literature is to rely on well-known general unsupervised machine-learning methods (e.g., k-means, hierarchical, spectral, DBSCAN) to select the most representative structure and number of day-types based solely on internal evaluation indices. These are easy to calculate but are limited since they only use information in the clustered dataset itself. In addition, the quality of clustering should ideally be demonstrated by external validation criteria, by expert assessment or the performance in its intended application. The main contribution of this paper is to test and compare the common practice of internal validation with external validation criteria represented by the application to short-term prediction, which also serves as a proxy for more general traffic management applications. When compared to external evaluation using short-term prediction, internal evaluation methods have a tendency to underestimate the number of representative day-types needed for the application. Additionally, the paper investigates the impact of using dimensionality reduction. By using just 0.1\% of the original dataset dimensions, very similar clustering and prediction performance can be achieved, with up to 20 times lower computational costs, depending on the clustering method. K-means and agglomerative clustering may be the most scalable methods, using up to 60 times fewer computational resources for very similar prediction performance to the p-median clustering.
|
|
| 18. |
- Cebecauer, Matej, et al.
(författare)
-
Revealing representative day-types in transport networks using traffic data clustering
- 2023
-
Ingår i: Journal of Intelligent Transportation Systems / Taylor & Francis. - : Informa UK Limited. - 1547-2450 .- 1547-2442. ; , s. 1-24
-
Tidskriftsartikel (refereegranskat)abstract
- Recognition of spatio-temporal traffic patterns at the network-wide level plays an important role in data-driven intelligent transport systems (ITS) and is a basis for applications such as short-term prediction and scenario-based traffic management. Common practice in the transport literature is to rely on well-known general unsupervised machine-learning methods (e.g., k-means, hierarchical, spectral, DBSCAN) to select the most representative structure and number of day-types based solely on internal evaluation indices. These are easy to calculate but are limited since they only use information in the clustered dataset itself. In addition, the quality of clustering should ideally be demonstrated by external validation criteria, by expert assessment or the performance in its intended application. The main contribution of this paper is to test and compare the common practice of internal validation with external validation criteria represented by the application to short-term prediction, which also serves as a proxy for more general traffic management applications. When compared to external evaluation using short-term prediction, internal evaluation methods have a tendency to underestimate the number of representative day-types needed for the application. Additionally, the paper investigates the impact of using dimensionality reduction. By using just 0.1% of the original dataset dimensions, very similar clustering and prediction performance can be achieved, with up to 20 times lower computational costs, depending on the clustering method. K-means and agglomerative clustering may be the most scalable methods, using up to 60 times fewer computational resources for very similar prediction performance to the p-median clustering.
|
|
| 19. |
- Cebecauer, Matej, 1986-
(författare)
-
Short-Term Traffic Prediction in Large-Scale Urban Networks
- 2019
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- City-wide travel time prediction in real-time is an important enabler for efficient use of the road network. It can be used in traveler information to enable more efficient routing of individual vehicles as well as decision support for traffic management applications such as directed information campaigns or incident management. 3D speed maps have been shown to be a promising methodology for revealing day-to-day regularities of city-level travel times and possibly also for short-term prediction. In this paper, we aim to further evaluate and benchmark the use of 3D speed maps for short-term travel time prediction and to enable scenario-based evaluation of traffic management actions we also evaluate the framework for traffic flow prediction. The 3D speed map methodology is adapted to short-term prediction and benchmarked against historical mean as well as against Probabilistic Principal Component Analysis (PPCA). The benchmarking and analysis are made using one year of travel time and traffic flow data for the city of Stockholm, Sweden. The result of the case study shows very promising results of the 3D speed map methodology for short-term prediction of both travel times and traffic flows. The modified version of the 3D speed map prediction outperforms the historical mean prediction as well as the PPCA method. Further work includes an extended evaluation of the method for different conditions in terms of underlying sensor infrastructure, preprocessing and spatio-temporal aggregation as well as benchmarking against other prediction methods.
|
|
| 20. |
- Cebecauer, Matej, et al.
(författare)
-
Similarity and Interchangeability of Flow and Speed Data for Transport Network Day-Type Clustering and Prediction
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Prediction of future traffic states is an essential part of traffic management and intelligent transportation systems. Previous work has shown that spatio-temporal clustering of traffic data such as flows or speeds into network day-types improves both the performance and the robustness of traffic predictions. Since some data types may not be available at a network-wide level, or only for certain periods, this paper investigates how similar such representative day-types are if based on different data types. The similarity of day-type clusters is evaluated with qualitative calendar visualization and two quantitative metrics, the Adjusted Mutual Information (AMI) which considers day-to-cluster assignments, and a new proposed Centroids Similarity Score (CSS) which compares centroids. The paper also explores the impact on flow and speed prediction performance of substituting one data type for the other in the clustering or classification phases. Using microwave sensor data from the Stockholm motorway network, our findings show that clusterings based on flows and speeds and across a range of clustering methods have reasonably high similarity. CSS is found to be a more relevant similarity indicator than AMI in the prediction application context. By capturing more relevant traffic state information, flow-based clustering and classification are robust for both flow and speed predictions, while speed-based clustering significantly degrades flow prediction performance.
|
|
| 21. |
- Cebecauer, Matej, et al.
(författare)
-
Spatio-Temporal Partitioning of Large Urban Networks for Travel Time Prediction
- 2018
-
Ingår i: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC). - : IEEE. - 9781728103235 ; , s. 1390-1395
-
Konferensbidrag (refereegranskat)abstract
- The paper explores the potential of spatiotemporal network partitioning for travel time prediction accuracy and computational costs in the context of large-scale urban road networks (including motorways/freeways, arterials and urban streets). Forecasting in this context is challenging due to the complexity, heterogeneity, noisy data, unexpected events and the size of the traffic network. The proposed spatio-temporal network partitioning methodology is versatile, and can be applied for any source of travel time data and multivariate travel time prediction method. A case study of Stockholm, Sweden considers a network exceeding 11,000 links and uses taxi probe data as the source of travel times data. To predict the travel times the Probabilistic Principal Component Analysis (PPCA) is used. Results show that the spatio-temporal network partitioning provides a more appropriate bias-variance tradeoff, and that prediction accuracy and computational costs are improved by considering the proper number of clusters towards robust large-scale travel time prediction.
|
|
| 22. |
- Cebecauer, Matej, et al.
(författare)
-
Spatio-Temporal Public Transport Mode Share Estimation and Analysis Using Mobile Network and Smart Card Data
- 2023
-
Ingår i: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2543-2548
-
Konferensbidrag (refereegranskat)abstract
- Public transport plays a vital role in society and the urban environment. However, knowledge of its spatial and temporal shares is often limited to traditional travel surveys. Recently, there has been substantial progress in mobility data collection, including data from traffic, public transport, and mobile phones. Especially mobile network data is a large-scale and affordable source of high-level mobility records. Similarly, public transport smart cards or ticket validation data are being collected and made available in major cities. The contribution of this study is to unveil the potential of estimating public transport shares, by merging mobile and smart card data. Stockholm, Sweden, is used as a case study. We analyze and discuss spatio-temporal patterns of estimated public transport shares for Stockholm, using descriptive and cluster analysis. The typical representative day-types are revealed and analyzed. Finally, a regression analysis considering the weather and socioeconomic context is conducted. It provides a highly explanatory and predictive understanding of which factors impact the share of public transport in Stockholm. To conclude, combined mobile and smart card data offers a cost-efficient, large-scale, low spatio-temporal aggregation (capturing daily and hourly variations) alternative to traditional travel surveys for analyzing PT shares.
|
|
| 23. |
- Cebecauer, Matej, et al.
(författare)
-
Using flows or speeds in traffic pattern clustering and prediction : does the data type matter?
- 2022
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Data and knowledge of travel patterns play a key role in finding more cost-effective solutions and better utilization of existing resources to increase sustainability and decrease CO2 emissions, pollution, and noise. Understanding travel patterns and prediction of future traffic states is a central ingredient in Intelligent transport systems (ITS). Pre-clustering the data before applying the prediction models is a recommended practice. We consider in this work revealing day-to-day traffic regularities and grouping days into representative day-types based on their traffic similarities before training prediction models. Specifically for this presentation, we will present our recent work on day-type clusterings that concern the similarities and interchangeability of day-types recognized by flow and speed traffic measurements. We consider the speed and flow traffic measurements from the motorway control system in the highway system around Stockholm, Sweden. Different clustering methods are used and their performance is evaluated on short-term prediction models. The results reveal that day-types are similar across data types and clustering methods, and their similarity does not depend much on the number of clusters. As the baseline scenario, calendar-based day-types are used. The similarity is higher between flow and speed recognized day-types compare to calendar-based day-types. Considering short-term prediction performance, the data-driven day-types outperform calendar-based methods. However, for more sophisticated prediction models the difference becomes insignificant. The interchangeability of speeds and flows in traffic prediction is studied in a scenario where new days are classified into day-types based on speed observations. This could be particularly interesting for traffic management centers as speed observations may be collected in more affordable, sustainable, and scalable ways. However, results reveal that flow prediction is sensitive to whether the new day is classified to one of the clusters using speed instead of flow observations, and prediction performance is reduced by about 28%. This sensitivity can be overcome by using a more sophisticated prediction model. When classifying based on flow observations a more sophisticated model results in slight improvements in speed prediction.
|
|
| 24. |
|
|
| 25. |
- Gundlegård, David, et al.
(författare)
-
Multimodal trafikledning
- 2024
-
Ingår i: Sammanställning av referat från Transportforum 2024. - Linköping : Statens väg- och transportforskningsinstitut. ; , s. 80-81
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Fyrstegsprincipen innebär att infrastruktur i första hand optimeras och byggs om än nyproduceras. I kombination med en ökande urbanisering gör det att trafiksystem ofta hanteras på gränsen av sin kapacitet. Små förändringar i utbud kan då få stor effekt på systemets prestanda och stor samhällsekonomisk påverkan. Därför är det viktigt med bra beslutsunderlag och analysverktyg för styrning av trafiken. Målet med trafikledning är delvis att utvärdera åtgärdsplaner i realtid, men dess huvudmål är att minska effekterna av incidenter för att få ett effektivt trafikflöde genom att guida trafikanter.Nya system för att kombinera transportsätt, exempelvis Mobility as a Service (MaaS), ger nya möjligheter för trafikanter att skifta mellan olika färdmedel. Samtidigt ger stora mängder data från kollektivtrafiknätet, vägtrafiknätet och mobilnätet i kombination med nya metoder för att skatta resmönster fördelat på färdmedel möjligheter till en ny förståelse för multimodala resmönster i en stad. Multimodal trafikledning syftar till en integrerad trafikledning för personresor med bil- och kollektivtrafik. Projektet Multimodal trafikledning syftar till att ta fram nya metoder för att skatta multimodal efterfrågan samt färdmedels- och ruttval vid incidenter för att möjliggöra bättre incidenthantering genom multimodal trafikledning.Projektet grundar sig i kvantitativ analys och modellering med hjälp av olika datakällor, exempelvis MCS-data från motorvägsnätet, detaljerade GPS-data från INRIX, mobilnätsdata från Telia, biljettdata från SL, GPS-data och tidtabelldata (GTFS) från bussar och incidentdata från Trafik Stockholm. Datadriven modellering av ruttval och färdmedelsval används för att förstå ruttval och färdmedelsval för bil- och kollektivtrafik, som i sin tur kan användas för att skatta och prediktera multimodal efterfrågan. Klustring används för att identifiera typdagar och som grund till att skatta och prediktera resmönster i det multimodala trafiknätet. Resultaten från projektet visar på potential i att kombinera olika datakällor för datadriven analys av multimodala resmönster som kan ge insikter för mulitmodal trafikledning. De framtagna metoderna har testats på ett nätverk i Stockholm både under normaltillstånd och med en incident och visade lovande resultat. I nuläget finns relativt få akademiska publikationer inom området multimodal trafikledning, men ett antal nystartade EU-projekt antyder att intresset för området växer.
|
|
| 26. |
|
|
| 27. |
- Jenelius, Erik, Docent, 1980-, et al.
(författare)
-
Bilrestider i storstad: Variationsmönster och upplevd osäkerhet (VARIA) : Slutrapport för projekt som genomförts på uppdrag av Trafikverket
- 2020
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Den här rapporten utgör slutrapport för projektet Bilrestider i storstad: Variationsmönster och upplevd osäkerhet (VARIA) som genomförts på uppdrag av Trafikverket (TRV 2018/16380).Det övergripande syftet med det här projektet har varit att vidareutveckla metoder för samhällsekonomiska kalkyler för olika åtgärder i trafiksystemet. Projektet bidrar till detta mål genom att utveckla kunskap om hur olika komponenter i restidsvariation påverkar trafikanternas faktiska erfarenhet av restidsvariation, och trafikanternas upplevelse av systematisk restidsvariation och deras restidsosäkerhet. Forskningsfrågorna studeras i två delstudier. En delstudie fokuserar på trafikanternas förståelse av restidsvariation, och deras upplevda restidsosäkerhet. Denna delstudie baseras på ett teoretiskt ramverk och två datakällor: (1) en enkätstudie av hur tillfrågade bilister beskriver den restidsfördelning de förväntar sig (och därmed kan antas planera för), samt (2) empiriska data som beskriver hur den verkliga restiden längs samma rutter varierar i olika dimensioner.Den andra delstudien fokuserar på sambandet mellan hur restiden varierar i data som uppmätts generellt, och den restidsvariation som enskilda bilister faktiskt utsätts för. Här utnyttjas ett statistiskt ramverk och en tredje datakälla: restidsobservationer från olika rutter på enskild passagenivå med beständiga fordons-ID.En grundläggande svaghet med den första delstudien ligger i att respondenterna i sina uppgivna restider inte tycks ha avgränsat sig på det sätt som var avsikten (bara rena körtider mellan de uppgivna ändnoderna). Trots detta anser vi att det utvecklade analytiska ramverket gör det möjligt att dra vissa övergripande slutsatser med bäring på studiens inledande frågeställningar.Resultaten från den första delstudien stöder sammantaget hypotesen att resenärerna i gemen i huvudsak baserar sin planering och schemaläggning inför en specifik resa på mer generella restidsprediktioner, som är underbyggda av deras samlade erfarenhet, (snarare än specifika erfarenheter eller annan information om restiden på den specifika rutten). Resultaten visar också att förare som kan bygga under sinna uppskattningar med mer specifik erfarenhet, har minst lika svårt att förutse hur restiden varierar, såväl när det gäller ”systematisk” som ”slumpmässig” variation, som förare som saknar specifik erfarenhet från just den rutt för vilken restiden skall uppskattas. Respondenternas prediktioner av restidsvariation är sämre än deras prediktioner av förväntad restid. Detta stöder hypotesen att restidens faktiska variation för en viss specifik rutt, under en viss specifik tid på dagen ger en långt ifrån komplett bild av den restidsosäkerhet som resenärer måste ta höjd för i sin planering och schemaläggning. Om samhällsekonomiska värderingar av restidsosäkerhet tillämpas direkt på uppmätt (eller predicerad) restidsvariation, utan hänsyn till det komplexa sambandet dem emellan, kan värdet av minskad restidsvariation såväl över- som underskattas. I den andra delstudien har vi undersökt i vilken utsträckning fordonen som korsar en rutt är återkommande resenärer, och hur den andelen beror av olika attribut. Med hjälp av data från Bluetooth- och Wifi-sensorer under en tremånadersperiod har vi funnit att det genomsnittliga antalet resor per fordons-ID är högre mot staden på morgontoppen och ut från staden på eftermiddagen, vilket är förenligt med vetskapen att pendlingsresor tenderar att ha den högsta regelbundenheten över dagar. Vi har även föreslagit en modell för hastighetsfördelningar hos rutter, som separerar variationen i en komponent med variation mellan resenärer, och en komponent med variation inom varje resenär (”individuell erfarenhet av restidsvariation”). Resultaten av modellberäkningar visar att den relativa individuella (inom resenären) variationen är betydligt högre i pendlingsriktningen (mot staden på morgonen och ut från staden på eftermiddagen) och på rutter med hög trängselnivå. Trängsel tycks alltså vara den viktigaste faktorn som förklarar den relativa restidsosäkerheten. På grund av en viss omnumrering över tiden av fordons-ID i datan som använts är den exakta frekvensen med vilken resenärer använder en rutt samt storleken på variabiliteten mellan resenärer och inom varje resenär svåra att skatta. Den beräknade frekvensen är låg, vilket i och för sig är i linje med analyser av data från trängselavgiftsportaler. Eftersom omhashningen rimligen är oberoende av geografi och tid på dagen så har vi kunnat göra relativa studier mellan rutter och tidsperioder. Resultaten indikerar att det måste göras en åtskillnad mellan den variabilitet som en enskild resenär kan ha erfarenhet av, och den totala variabiliteten som vanligtvis används i bedömningarna av restiders tillförlitlighet. Den relativa storleken på de två termerna varierar systematiskt med ruttegenskaper och tidsperioder. Utan denna åtskillnad kan kostnaderna för restidsvariabilitet överskattas
|
|
| 28. |
- Jenelius, Erik, Docent, 1980-, et al.
(författare)
-
Impacts of COVID-19 on public transport ridership in Sweden: Analysis of ticket validations, sales and passenger counts
- 2020
-
Ingår i: Transportation Research Interdisciplinary Perspectives. - : Elsevier. - 2590-1982. ; 8
-
Tidskriftsartikel (refereegranskat)abstract
- The paper analyses the impacts of COVID-19 on daily public transport ridership in the three most populated regions of Sweden (Stockholm, Västra Götaland and Skåne) during spring 2020. The analysis breaks down the overall ridership with respect to ticket types, youths and seniors, and transport modes based on ticket validations, sales and passenger counts data. By utilizing disaggregate ticket validation data with consistent card ids we further investigate to what extent fewer people travelled, or each person travelled less, during the pandemic. The decrease in public transport ridership (40%–60% across regions) was severe compared with other transport modes. Ridership was not restricted by service levels as supply generally remained unchanged throughout the period. The ridership reduction stems primarily from a lower number of active public transport travellers. Travellers switched from monthly period tickets to single tickets and travel funds, while the use and the sales of short period tickets, used predominantly by tourists, dropped to almost zero. One-year period tickets and school tickets increased from mid-April, which could indicate that the travellers using these tickets are particularly captive to the public transport system. Collaborative effort is required to put the results in the international context.
|
|
| 29. |
|
|
| 30. |
- Jenelius, Erik, et al.
(författare)
-
SHARP: Pre-study of Data Sharing for Demand-Responsive and Public Transport System-of-Systems : Public report
- 2019
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Collaboration between demand-based and conventional fixed public transport can create a robust and predictable transport service system-of-systems (SoS). Sustainable business models in this area could help lower congestion, lower overall fossil fuel usage, decrease pollution, shorten travel times, and decrease costs of person-km, while increasing the revenue for transport providers. The purpose of this pre-study is to improve the efficiency, robustness and attractiveness of shared urban mobility by facilitating better real-time integration between fixed public transport services and demand-responsive taxi services.The pre-study organized a stakeholder workshop, which introduced two main use cases for public transport and taxi data sharing: (1) last-mile service and (2) disruption management. Last-mile service in low-density areas is a frequently highlighted application of integrated fixed and demand-responsive services. In such areas, taxis can serve as last-mile service connecting to a mass PT line.The second use case in disruption management. This pre-study explores the potential of a system-of-systems in which the PTA enters an agreement with one or several taxi companies to provide bridging services with taxis during PT disruptions. The purpose of the collaboration is to complement and potentially reduce the need of bridging buses, reduce the costs of reimbursements and potentially fully replace them, reduce the waiting times and the responsibilities of the individual travellers to arrange alternative modes of transport. Through real-time sharing of travel demand data the SoS framework can better utilize available taxi capacity through proactive dispatching and ride sharing. Real-world examples and simulation studies suggest that such an approach can successfully reduce traveler waiting times and environmental impacts, whereas the cost savings for the PTA need further study.The preliminary data analysis aimed to investigate the potential of integrating available public transport and taxi data sources. To demonstrate the effects of public transport disruptions, we selected a case study involving a disruption of 23 metro stations located on the red line in Stockholm. Taxi and public transport demand data from one historical day (1st April 2016) were used. The data are used to simulate the effects of different disruption management strategies on passenger waiting times and operational costs. A strategy where the public transport authority coordinates taxi journeys leads to considerably shorter waiting times and lower costs compared to if the travelers themselves book the trips.The stakeholder discussions, literature review and data analysis suggest that a system-of- systems involving taxi and public transport is a promising approach for public transport disruption management. Further research is needed to develop the business models of SoS for disruption management in a Swedish setting. Further work should also be directed towards a pilot study.
|
|
| 31. |
|
|
| 32. |
- Kholodov, Yaroslav, et al.
(författare)
-
Public transport fare elasticities from smartcard data: Evidence from a natural experiment
- 2021
-
Ingår i: Transport Policy. - : Elsevier BV. - 0967-070X .- 1879-310X. ; 105, s. 35-43
-
Tidskriftsartikel (refereegranskat)abstract
- This paper develops a method for analysing the elasticity of travel demand to public transport fares. The methodology utilizes public transport smartcard data for collecting disaggregate full population data about passengers’ travel behaviour. The study extends previous work by deriving specific fare elasticities for distinct socioeconomic (e.g., car ownership and income) groups and public transport modes (metro, trains and buses), and by considering the directionality of the fare change. The case study involves a public transport fare policy introduced by the regional administration of Stockholm County in January 2017, where the zonal fare system for single-trip tickets was replaced by a flat-fare policy. The overall fare elasticity of travel funds is found to be −0.46. User sensitivity grows along with the journey distance. Metro users demonstrate the lowest sensitivity, followed by bus and commuter train riders. Low socioeconomic groups, in particular with respect to car ownership, tend to be less sensitive than the high-factor groups. In addition to the direct effect of changed fares, simplification and unification of the fare scheme appears to have substantially contributed to its attractiveness. The flat fare may allow the geographic disparity of public transport travel to be reduced and new users to be attracted from remote areas who are more prone to own cars.
|
|
| 33. |
- Kholodov, Yaroslav, et al.
(författare)
-
Transport fare elasticities from smartcard data : A natural experiment in Stockholm
- 2021
-
Konferensbidrag (refereegranskat)abstract
- This paper develops a method for analysing the elasticity of travel demand to public transport fares. The methodology utilizes public transport smartcard data for collecting disaggregate, full population data about passengers’ travel behaviour. The study extends previous work by deriving specific fare elasticities for distinct socioeconomic (e.g., car ownership and income) groups and public transport modes (metro, trains and buses), and by considering the directionality of the fare change. The case study involves a public transport fare policy introduced by the regional administration of Stockholm County in January 2017, where the zonal fare system was replaced by a flat-fare policy. The overall fare elasticity of travel funds is found to be -0.46. User sensitivity grows along with the journey distance. Metro users demonstrate the lowest sensitivity, followed by bus and commuter train riders. Low socioeconomic groups are sensitive to a price increase and do not adjust their behaviour with a price decrease, whereas the high-factor groups’ sensitivity is the opposite. In addition to the direct effect of changed fares, simplification and unification of the fare scheme appears to have substantially contributed to its attractiveness. The flat fare may allow the geographic disparity of public transport travel to be reduced and new users to be attracted from remote areas who are more prone to own cars.
|
|
| 34. |
- Koháni, M., et al.
(författare)
-
Designing charging infrastructure for a fleet of electric vehicles operating in large urban areas
- 2017
-
Ingår i: ICORES 2017 - Proceedings of the 6th International Conference on Operations Research and Enterprise Systems. - : SciTePress. - 9789897582189 ; , s. 360-368
-
Konferensbidrag (refereegranskat)abstract
- Here, we propose a method to design a charging infrastructure for a fleet of electric vehicles such as a fleet of taxicabs, fleet of vans used in the city logistics or a fleet of shared vehicles, operating in large urban areas. Design of a charging infrastructure includes decisions about charging stations location and number of charging points at each station. It is assumed that the fleet is originally composed of vehicles equipped with an internal combustion engine, however, the operator is wishing to replace them with fully electric vehicles. To avoid an interaction with other electric vehicles it is required to design a private network of charging stations that will be specifically adapted to the operation of a fleet. It is often possible to use GPS traces of vehicles characterizing actual travel patterns of individual vehicles. First, to derive a suitable set of candidate locations from GPS data, we propose a practical procedure where the outcomes can be simply controlled by setting few parameter values. Second, we formulate a mathematical model that combines location and scheduling decisions to ensure that requirements of vehicles can be satisfied. We validate the applicability of our approach by applying it to the data characterizing a large taxicab fleet operating in the city of Stockholm. Our results indicate that this approach can be used to estimate the minimal requirements to set up the charging infrastructure.
|
|
| 35. |
- Koháni, M., et al.
(författare)
-
Location-scheduling optimization problem to design private charging infrastructure for electric vehicles
- 2018
-
Ingår i: 6th International Conference on Operations Research and Enterprise Systems, ICORES 2017. - Cham : Springer. - 9783319947662 ; , s. 151-169
-
Konferensbidrag (refereegranskat)abstract
- We propose optimization model to design a charging infrastructure for a fleet of electric vehicles. Applicable examples include a fleet of vans used in the city logistics, a fleet of taxicabs or a fleet of shared vehicles operating in urban areas. Fleet operator is wishing to replace vehicles equipped with an internal combustion engine with fully electric vehicles. To eliminate interaction with other electric vehicles it is required to design a private network of charging stations that is specifically adjusted to the fleet operation. First, to derive a suitable set of candidate locations from GPS data, we propose a practical procedure where the outcomes can be simply controlled by setting few parameter values. Second, we formulate a mathematical model that combines location and scheduling decisions to ensure that requirements of vehicles can be satisfied. We validate the applicability of our approach by applying it to data characterizing a large taxicab fleet operating in the city of Stockholm. The model assumes that all vehicles posses complete information about all other vehicles. To study the role of available information, we evaluate the resulting designs considering the coordinated charging when vehicle drivers, for example, reveal to each other departure times, and the uncoordinated charging when vehicle drivers know only actual occupation of charging points. Our results indicate that this approach can be used to estimate the minimal requirements to set up the charging infrastructure.
|
|
| 36. |
- Kolkowski, Lukas, et al.
(författare)
-
Measuring activity-based social segregation using public transport smart card data
- 2023
-
Ingår i: Journal of Transport Geography. - : Elsevier BV. - 0966-6923 .- 1873-1236. ; 110
-
Tidskriftsartikel (refereegranskat)abstract
- While social segregation is often assessed using static data concerning residential areas, the extent to which people with diverse background travel to the same destinations may offer an additional perspective on the extent of urban segregation. This study further contributes to the measurement of activity-based social segregation between multiple groups using public transport smart card data. In particular, social segregation is quantified using the ordinal information theory index to measure the income group mix at public transport journey destination zones. The method is applied to the public transport smart card data of Stockholm County, Sweden. Applying the index on 2017-2020 data sets for a selected week, shows significant differences between income groups' segregation along the radial public transport corridors following the opening of a major rail project in the summer of 2017. The overall slight decrease in segregation over the years can be linked to declining segregation in the city center as a travel destination and its public transport hubs. Increasing zonal segregation is observed in suburban and rural zones with commuter train stations. This method helps to quantify social segregation, enriching the analysis of urban segregation and can aid in evaluating policies based on the dynamics of social life.
|
|
| 37. |
|
|
| 38. |
- Langbroek, Joram H. M., et al.
(författare)
-
Electric vehicle rental and electric vehicle adoption
- 2019
-
Ingår i: Research in Transportation Economics. - : Elsevier. - 0739-8859 .- 1875-7979. ; 73, s. 72-82
-
Tidskriftsartikel (refereegranskat)abstract
- This case study describes the project Elbilsiandet (The Electric Vehicle Country) in Gotland, Sweden, where the island Gotland is made "ready for electric vehicles" by providing a network of charging infrastructure and electric vehicle rental during several summer seasons. The influence of the electric vehicle (EV) rental scheme on the process towards electric vehicle adoption is investigated using the Protection Motivation Theory (PMT) and the Transtheoretical Model of Change (TTM). Moreover, the travel patterns of electric rental cars are compared with those of conventional rental cars. The main results of this study are the following: Firstly, people renting an EV are on average closer to electric vehicle adoption than people renting a conventional vehicle. Secondly, people who rent an EV are at the time of rental associated with more positive attitudes towards EVs, have more knowledge about EVs and would feel more secure driving an EV. Thirdly, EV-rental does not seem to have a large additional effect on the stage-of-change towards EV-adoption of the participants. Lastly, the driving patterns of EVs do not seem to indicate serious limitations regarding driving distance, parking time and the destinations that have been visited, as compared to the driving patterns of conventional rental cars.
|
|
| 39. |
|
|
| 40. |
|
|
| 41. |
- Lin, Joanne Yuh-Jye, et al.
(författare)
-
The equity of public transport crowding exposure
- 2023
-
Ingår i: Journal of Transport Geography. - : Elsevier BV. - 0966-6923 .- 1873-1236. ; 110
-
Tidskriftsartikel (refereegranskat)abstract
- Public transport crowding exposure is known to cause discomfort, stress and dissatisfaction. However, the distribution and equity of crowding exposure across socioeconomic groups has been largely unexplored. This paper opens a new research topic connecting crowding exposure in public transport to travelers’ socioeconomic characteristics. We present a framework for assessing the equity of in-vehicle crowding exposure based on automatic data sources. Two metrics are considered for quantifying the travelers’ in-vehicle crowding exposure: (1) the excess perceived travel time and (2) the relative excess perceived travel time. The proposed methodology computes the two metrics based on travel diaries and in-vehicle loads inferred from automated fare collection data. We implement Lorenz curves, Gini and Suits coefficients to evaluate horizontal (across the population) and vertical equity (considering income as well as mobility ability and need). The vertical equity is further discussed using clusters of socioeconomic groups and results from spatial lag regression models to assess the distribution of crowding exposure across socioeconomic characteristics. The results for the Stockholm Region case study indicate that crowding exposure varies substantially across the service area, with the highest values found in the denser urban areas close to Stockholm City. We find that the distribution across socioeconomic groups is relatively even, but travelers from areas that are wealthier, higher educated, have higher share of rental housing or lower vehicle ownership areas tend to be exposed to more crowding. The paper provides tools to support public transport planners in decision-making, showing where to intervene to reduce crowding exposure efficiently to achieve urban equity and sustainability.
|
|
| 42. |
- Ngo, Ha Nhi, et al.
(författare)
-
Considering Multi-Scale Data for Continuous Traffic Prediction Using Adaptive Multi-Agent System
- 2023
-
Ingår i: Proceedings 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1835-1842
-
Konferensbidrag (refereegranskat)abstract
- Accurate traffic prediction is essential for effective traffic management and planning. However, traffic prediction models are challenged by various factors such as complex spatiotemporal dependencies in traffic data. Recently, researchers have explored the new approach known as stream analysis since it can continuously update models by capturing new behaviors from the traffic data stream. However, applying this approach specifically raises the question about the balance between model complexity and model flexibility for dynamic updates. ADRIP - Adaptive multi-agent system for DRIving behaviors Prediction proposed in [1], [2] has combined the dynamic clustering and the multi-agent system approach to solve this challenge. This system has been applied to predict traffic dynamics at the road segment level. In this paper, we aim to extend ADRIP to complete its functionality for traffic prediction at the network level. Experiments for multi-scale traffic data are conducted to compare extended ADRIP with well-known clustering models.
|
|
| 43. |
- Rosina, K., et al.
(författare)
-
Using OpenStreetMap to improve population grids in Europe
- 2016
-
Ingår i: Cartography and Geographic Information Science. - : Informa UK Limited. - 1523-0406 .- 1545-0465. ; , s. 1-13
-
Tidskriftsartikel (refereegranskat)abstract
- OpenStreetMap (OSM) database has previously been used to support spatial disaggregation of population data by partly masking out non-residential impervious areas in the European Copernicus imperviousness layer (IL). However, the exact procedure of OSM data incorporation is unknown, and its contribution to the improvement of estimation accuracy has never been studied. In this article, we present a sensitivity study to find out which road categories should be used for masking of IL and how the linear features might be transformed to raster representation. Using Austria and Slovenia as a study area, 2006 commune population counts are disaggregated into 100 m grid cells using 12 versions of modified IL. Further tuning of estimates is performed using CORINE Land Cover (CLC) data in an iterative algorithm. Disaggregated grids are then validated against reference 1 km census-based data. The results show that overall error was reduced thanks to OSM incorporation in all tested scenarios, although the relative improvement varies between as well as within the two countries. The best result (5.3% reduction) was achieved using railways and three major road categories (motorway, trunk, and primary) with double exaggeration of width.
|
|
| 44. |
- Rubensson, Isak, et al.
(författare)
-
Resmönster och fördelningseffekter av taxeförändringanalyserat med hjälp av biljettvalideringsdata
- 2020
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Bakgrund: Hur människor reser med kollektivtrafik har historiskt främst analyserats med hjälp av enkäter där svarande har fått ange sitt resande under en slumpmässigt vald resdag. Sådana resvaneundersökningar blir dock mer och mer problematiska att använda då svarsfrekvenserna trendmässigt faller vilket gör att fler enkäter behöver delas ut (vilket ger högre kostnader) samt att den resulterande kunskapen om resvanor blir mindre representativ. En alternativ väg för att förstå resenärers resmönster har öppnat sig i och med inträdet av smarta kort som biljettbärare, med ett sådant kort registreras resor vid biljettvalidering och den informationen kan därefter studeras och analyseras. I region Stockholm har smarta kort (accesskort) använts i kollektivtrafiken sedan 2011 men inte förrän under senare år börjat studerats för att nå förståelse kring resvanemönster. Ett hinder i analysen har varit att korten är så kallade tap-in kort, det vill säga att det endast är vid en resas start som kortet registreras inte vid deras slut. Dessbättre har det internationellt under de senaste åren vuxit fram en välfungerande metodik för att från en serie av inträden i kollektivtrafiken, tillsammans med annan data, sluta sig till var resan slutar samt om det skett några eventuella byten på vägen. Sådan metodik har visats kunna med framgång bestämma uppemot 80-90% av alla registrerade resors rutt och destination.Metod: I det här projektet som varit ett samarbete mellan Trafikförvaltningen Region Stockholm, KTH och universitetet TU Delft, Nederländerna har resandet med kollektivtrafik i Stockholm analyserats före och efter de taxe-förändringar som genomfördes i januari 2017, då systemets biljettpriser höjdes generellt och zoner togs bort för enkelbiljetter (det var redan tidigare zon-löst för periodbiljetter). Priselasticiteter (hur mycket resandet förändras vid en prisförändring) har beräknats. Vidare har fördelningseffekter av prisförändringarna studerats, dels genom att jämföra priselasticiteter för de som rest för reducerat biljettpris (barn, studerande, pensionärer) med elasticiteter för de som rest för fullt pris. Dels genom att studera hur antalet resor per capita skiljer sig mellan resande från basområden (Stockholmsregionen består av 1300 basområden) med hög och låg medelinkomst, högt och lågt antal bilar per capita, på långt eller kort avstånd från regioncentrum.Resultat och slutsats: Vi finner att elasticiteter rör sig i ett historiskt vanligt spann för kollektivtrafik kring -0,3 till -0,6 (en elasticitet på -0,1 betyder att resandet minskar med 1% om priset höjs med 10%). Att de som reser på reducerad biljett, bor i områden med låg inkomst eller områden med lågt bilinnehav har lägre priskänslighet än de som reser på fullt pris, bor i höginkomsttagarområden eller har tillgång till bil. De med högre inkomster reser även fler resor än de med lägre inkomster. Vi tolkar det som att de med alternativ möjlighet att resa har lägre priskänslighet än de som är hänvisade till kollektivtrafiken. Det föreliggande projektet har varit en pilot för att visa hur kraftfullt analyser av resandemönster med hjälp av data från smarta kort är och förhoppningen är att dessa metoder kan komma att bli en permanent del i hur kollektivtrafikmyndigheter generellt använder informationen i biljettsystemet för planering och förbättring av kollektivtrafiksystemet.
|
|
| 45. |
|
|
| 46. |
|
|
| 47. |
- Skoufas, Anastasios, et al.
(författare)
-
Generating and Evaluating Route Choice Sets for Large Multimodal Public Transport Networks : A Case Study for Stockholm Region
- 2023
-
Ingår i: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2926-2931
-
Konferensbidrag (refereegranskat)abstract
- Identification of Choice Sets (CSs) is a crucial step towards the estimation of public transport route choice models. However, identification of reliable CSs is a challenging task as the routes considered by travelers are not directly observed. To handle this issue, this study adopts an existing Choice Set Generation Methodology (CSGM) for the identification of CSs by using General Transit Feed Specification (GTFS) data. The final feasible CSs are then compared to the actual passengers' choices observed in Smart Card Data (SCD) by using three validation metrics; passenger and network coverage as well as network efficiency. The aim of the study is to shed light on the performance of a CSGM in Stockholm's multimodal transit network by using data retrieved by different Intelligent Transportation System (ITS) applications focused on operations and on ridership automated data collection. However, the use of CSGM for large networks raises scalability and computational issues. In this direction, the study also contributes in the implementation of the CSGM in a larger network compared to existing case studies.
|
|
| 48. |
|
|
| 49. |
|
|
| 50. |
- Tympakianaki, Athina, et al.
(författare)
-
Impact analysis of transport network disruptions using multimodal data : A case study for tunnel closures in Stockholm
- 2018
-
Ingår i: Case Studies on Transport Policy. - : ELSEVIER SCIENCE BV. - 2213-624X .- 2213-6258. ; 6:2, s. 179-189
-
Tidskriftsartikel (refereegranskat)abstract
- The paper explores the utilization of heterogeneous data sources to analyze the multimodal impacts of transport network disruptions. A systematic data-driven approach is proposed for the analysis of impacts with respect to two aspects: (a) spatiotemporal network changes, and (b) multimodal effects. The feasibility and benefits of combining various data sources are demonstrated through a case study for a tunnel in Stockholm, Sweden which is often prone to closures. Several questions are addressed including the identification of impacted areas, and the evaluation of impacts on network performance, demand patterns and performance of the public transport system. The results indicate significant impact of tunnel closures on the network traffic conditions due to the redistribution of vehicles on alternative paths. Effects are also found on the performance of public transport. Analysis of the demand reveals redistribution of traffic during the tunnel closures, consistent with the observed impacts on network performance. Evidence for redistribution of travelers to public transport is observed as a potential effect of the closures. Better understanding of multimodal impacts of a disruption can assist authorities in their decision-making process to apply adequate traffic management policies.
|
|
