| 1. |
- Hu, M., et al.
(författare)
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Decision Tree-Based Maneuver Prediction for Driver Rear-End Risk-Avoidance Behaviors in Cut-In Scenarios
- 2017
-
Ingår i: Journal of Advanced Transportation. - : Hindawi Limited. - 0197-6729 .- 2042-3195. ; 2017, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- Predicting driver rear-end risk-avoidance maneuvers in cut-in scenarios, especially dangerous precrash scenarios, benefits the customization of automatic driving, particularly automatic steering. This paper studies driver rear-end risk-avoidance behaviors in cut-in scenarios on a straight three-lane highway. Data from 24 participants in 1326 valid trials were collected using amotion-based driving simulator. An Eysenck Personality Questionnaire (revised for Chinese participants) was used to obtain the personality traits of the participants. Based on a statistical analysis, the candidate features used in the driver maneuver prediction were determined as a combination of objective risk indicators and driver characteristics. A decision tree-based model was constructed for maneuver prediction in cut-in scenarios. The prediction accuracy of the extracted classification rules was 79.2% for the training data set and 80.3% for the test data set. The most powerful predictive variables were extracted, and their effects on maneuver decisions were analyzed. The results show that driver characteristics strongly influence the prediction of maneuver decisions.
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| 2. |
- Li, Guofa, et al.
(författare)
-
Detection of road traffic participants using cost-effective arrayed ultrasonic sensors in low-speed traffic situations
- 2019
-
Ingår i: Mechanical Systems and Signal Processing. - : Elsevier BV. - 0888-3270 .- 1096-1216. ; 132, s. 535-545
-
Tidskriftsartikel (refereegranskat)abstract
- Effective detection of traffic participants is crucial for driver assistance systems. Traffic safety data reveal that the majority of preventable pedestrian fatalities occurred at night. The lack of light at night may cause dysfunction of sensors like cameras. This paper proposes an alternative approach to detect traffic participants using cost-effective arrayed ultrasonic sensors. Candidate features were extracted from the collected episodes of pedestrians, cyclists, and vehicles. A conditional likelihood maximization method based on mutual information was employed to select an optimized subset of features from the candidates. The belonging probability to each group along with time was determined based on the accumulated object type attributes outputted from a support vector machine classifier at each time step. Results showed an overall detection accuracy of 86%, with correct detection rate of pedestrians, cyclists and vehicles around 85.7%, 76.7% and 93.1%, respectively. The time needed for detection was about 0.8 s which could be further shortened when the distance between objects and sensors was shorter. The effectiveness of arrayed ultrasonic sensors on objects detection would provide all-around-the-clock assistance in low-speed situations for driving safety.
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| 3. |
- Li, G., et al.
(författare)
-
Driver reliance characteristics on forward collision warning systems in adjacent vehicle cut-in situations
- 2020
-
Ingår i: Journal of Automotive Safety and Energy. ; 11:1, s. 36-43
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Tidskriftsartikel (refereegranskat)abstract
- An evaluation method was investigated to assess driver reliance characteristics on forward collision warning systems based on a driving simulator to improve driving safety in adjacent vehicle cut-in situations. Using alarm timing (time to collision, TTC) as the control variable, driving behavior data from 12 participants were collected in adjacent vehicle cut-in situations. Two objective indexes (brake reliance index and secondary task index) and two subjective indexes (risk level index and trust level index) were proposed to establish the evaluation system model to realize the quantitative evaluation of driver reliance level on the systems. An L9(34) orthogonal experiment was designed and conducted. Regression models of driver reliance indexes were established. The results show that alarm timing is the most significant factor affecting driver reliance. A late alarm (TTC = 2.4 s) degrades the effectiveness of the systems, while an early alarm (TTC = 1.2 s) causes drivers’ over-reliance on the systems. Therefore, appropriately delaying the alarm timing (TTC = 1.8 s) can improve driver reliance for safety considerations.
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| 4. |
- Li, Guofa, et al.
(författare)
-
Traffic Crash Characteristics in Shenzhen, China from 2014 to 2016
- 2021
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Ingår i: International Journal of Environmental Research and Public Health. - : MDPI AG. - 1661-7827 .- 1660-4601. ; 18:3, s. 1-24
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Tidskriftsartikel (refereegranskat)abstract
- Road traffic crashes cause fatalities and injuries of both drivers/passengers in vehicles and pedestrians outside, thus challenge public health especially in big cities in developing countries like China. Previous efforts mainly focus on a specific crash type or causation to examine the crash characteristics in China while lacking the characteristics of various crash types, factors, and the interplay between them. This study investigated the crash characteristics in Shenzhen, one of the biggest four cities in China, based on the police-reported crashes from 2014 to 2016. The descriptive characteristics were reported in detail with respect to each of the crash attributes. Based on the recorded crash locations, the land-use pattern was obtained as one of the attributes for each crash. Then, the relationship between the attributes in motor-vehicle-involved crashes was examined using the Bayesian network analysis. We revealed the distinct crash characteristics observed between the examined levels of each attribute, as well the interplay between the attributes. This study provides an insight into the crash characteristics in Shenzhen, which would help understand the driving behavior of Chinese drivers, identify the traffic safety problems, guide the research focuses on advanced driver assistance systems (ADASs) and traffic management countermeasures in China.
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| 5. |
- Liao, Yuan, 1991, et al.
(författare)
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A Mobility Model for Synthetic Travel Demand from Sparse Traces
- 2022
-
Ingår i: IEEE Open Journal of Intelligent Transportation Systems. - 2687-7813. ; 3, s. 665-678
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Tidskriftsartikel (refereegranskat)abstract
- Knowing how much people travel is essential for transport planning. Empirical mobility traces collected from call detail records (CDRs), location-based social networks (LBSNs), and social media data have been used widely to study mobility patterns. However, these data suffer from sparsity, an issue that has largely been overlooked. In order to extend the use of these low-cost and accessible data, this study proposes a mobility model that fills the gaps in sparse mobility traces from which one can later synthesise travel demand. The proposed model extends the fundamental mechanisms of exploration and preferential return to synthesise mobility trips. The model is tested on sparse mobility traces from Twitter. We validate our model and find good agreement on origin-destination matrices and trip distance distributions for Sweden, the Netherlands, and Saõ Paulo, Brazil, compared with a benchmark model using a heuristic method, especially for the most frequent trip distance range (1-40 km). Moreover, the learned model parameters are found to be transferable from one region to another. Using the proposed model, reasonable travel demand values can be synthesised from a dataset covering a large enough population of very sparse individual geolocations (around 1.5 geolocations per day covering 100 days on average).
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| 6. |
- Liao, Yuan, 1991, et al.
(författare)
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Context-Adaptive support information for truck drivers: An interview study on its contents priority
- 2017
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Ingår i: 28th IEEE Intelligent Vehicles Symposium, IV 2017, Redondo Beach, United States, 11-14 June 2017. - 9781509048045 ; , s. 1268-1273
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Konferensbidrag (refereegranskat)abstract
- Truck drivers is a key group to promote road safety. For them, proper priority ranking scheme of content adaptation design benefits the high system effectiveness of in-vehicle driver decision support. Taking Chinese truck drivers as an example, the present study revealed the context-Adaptive support information from the perspective of truck drivers; their perceptions of in-vehicle information contents priority in 6 typical driving contexts. Data of 19 participants from 7 logistics companies were collected using a simulation interview method. Based on qualitative summary and statistical analysis, the results are summarized in two aspects; contextual information priority and impacts of driving experience on it. From the perspective of truck driver requirement, these results provide references for the design of context-Adaptive driver decision support.
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| 7. |
- Liao, Yuan, 1991, et al.
(författare)
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Cross-regional driver-vehicle interaction design: An interview study on driving risk perceptions, decisions, and ADAS function preferences
- 2018
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Ingår i: IET Intelligent Transport Systems. - : Institution of Engineering and Technology (IET). - 1751-9578 .- 1751-956X. ; 12:8, s. 801-808
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Tidskriftsartikel (refereegranskat)abstract
- A cross-regional study of driving behaviour and technological preferences in typical driving scenarios, especially dangerous pre-crash scenarios, is presented as a contribution to the user experience design of in-vehicle driver assistance functions. Data from 46 participants are collected by one-to-one interviews following viewing of 11 video clips previously obtained from NFOT (Naturalistic Field Operational Tests) and representative of typical real driving scenarios. Six questions relating to each driving scenario are asked to reveal the differences between Chinese drivers and Swedish drivers. The results show similarities and differences in driving risk perceptions, decisions, and preferences concerning the assistance and specifics of potential ADAS functions of drivers in China and Sweden. The preferences for assistance and ADAS functions are found to be correlated with relative driving risk perceptions and decisions in typical driving scenarios for both country groups. Based on the results, some suggestions for the design of driver-vehicle interactions for Chinese drivers are presented.
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| 8. |
- Liao, Yuan, 1991, et al.
(författare)
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Cross-regional Study on Driver Response Behaviour Patterns and System Acceptance with Triggered Forward Collision Warning
- 2017
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Ingår i: 2017 IEEE Intelligent Vehicles Symposium. - 9781509048045 ; , s. 565-570
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Konferensbidrag (refereegranskat)abstract
- Understanding complex behavior patterns in response to triggered forward collision warning system benefits localized user experience design, especially in safety critical scenarios from a cross-regional perspective. This paper studies driver response behavior patterns towards two alarm timings, in car-following scenarios with different traffic density. Data from 32 participants in China and 30 participants in Sweden were collected using a driving simulator. Differences were observedbetween China group and Sweden group regarding responsebehavior patterns, system acceptance and effectiveness. Seen from obtained results, Chinese drivers were found to steer more frequently than Swedish drivers in response to triggered alarm no matter what alarm timing or traffic density there were. Chinese drivers preferred later alarm timing than Swedish drivers. To better design regional-adaptive human machine interaction of forward collision warning system; some suggestions were produced based on obtained results.
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| 9. |
- Liao, Yuan, 1991, et al.
(författare)
-
Detection of Driver Cognitive Distraction: A Comparison Study of Stop-Controlled Intersection and Speed-Limited Highway
- 2016
-
Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 17:6, s. 1628-1637
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Tidskriftsartikel (refereegranskat)abstract
- Driver distraction has been identified as one major cause of unsafe driving. The existing studies on cognitive distraction detection mainly focused on high-speed driving situations, but less on low-speed traffic in urban driving. This paper presents a method for the detection of driver cognitive distraction at stop-controlled intersections and compares its feature subsets and classification accuracy with that on a speed-limited highway. In the simulator study, 27 subjects were recruited to participate. Driver cognitive distraction is induced by the clock task that taxes visuospatial working memory. The support vector machine (SVM) recursive feature elimination algorithm is used to extract an optimal feature subset out of features constructed from driving performance and eye movement. After feature extraction, the SVM classifier is trained and cross-validated within subjects. On average, the classifier based on the fusion of driving performance and eye movement yields the best correct rate and F-measure (correctrate = 95.8 ± 4.4%; for stop-controlled intersections and correct rate = 93.7 ± 5.0%; for a speed-limited highway) among four types of the SVM model based on different candidate features. The comparisons of extracted optimal feature subsets and the SVM performance between two typical driving scenarios are presented.
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| 10. |
- Liao, Yuan, 1991, et al.
(författare)
-
Detection of driver cognitive distraction: An SVM based real-time algorithm and its comparison study in typical driving scenarios
- 2016
-
Ingår i: IEEE Intelligent Vehicles Symposium, Proceedings. 2016 IEEE Intelligent Vehicles Symposium, IV 2016; Gotenburg; Sweden; 19-22 June 2016. - 9781509018215 ; 2016-August:Art no 7535416, s. 394-399
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Konferensbidrag (refereegranskat)abstract
- Detection of driver cognitive distraction is critical for active safety systems of road vehicles. Compared with visual distraction, cognitive distraction is more challenging for detection due to the lack of apparent exterior features. This paper presents a novel real-time detection algorithm for driver cognitive distraction by using support vector machine (SVM). Data are collected from 26 subjects, driving in typical urban and highway scenarios in a simulator. The chosen urban scenario is the stop-controlled intersection and the highway scenario is the speed-limited highway. Driver cognitive distraction while driving is induced by clock tasks which compete with the main driving tasks for visuospatial short working memory. For each subject, distracted driving instances and the equal number of non-distracted driving instances were collected (24 for urban scenario and 20 for highway scenario in total). Features concerning both driving performance and eye movement are used for training and validation. The proposed algorithm have correct rate of 93.0% and 98.5% for highway and urban scenarios respectively. Results also show that driver distraction can be recognized 6.5 s to 9.0 s after its happening, indicating good performance of the detection algorithm.
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| 11. |
- Liao, Yuan, 1991, et al.
(författare)
-
Disparities in travel times between car and transit: Spatiotemporal patterns in cities
- 2020
-
Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322 .- 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Cities worldwide are pursuing policies to reduce car use and prioritise public transit (PT) as a means to tackle congestion, air pollution, and greenhouse gas emissions. The increase of PT ridership is constrained by many aspects; among them, travel time and the built environment are considered the most critical factors in the choice of travel mode. We propose a data fusion framework including real-time traffic data, transit data, and travel demand estimated using Twitter data to compare the travel time by car and PT in four cities (São Paulo, Brazil; Stockholm, Sweden; Sydney, Australia; and Amsterdam, the Netherlands) at high spatial and temporal resolutions. We use real-world data to make realistic estimates of travel time by car and by PT and compare their performance by time of day and by travel distance across cities. Our results suggest that using PT takes on average 1.4–2.6 times longer than driving a car. The share of area where travel time favours PT over car use is very small: 0.62% (0.65%), 0.44% (0.48%), 1.10% (1.22%) and 1.16% (1.19%) for the daily average (and during peak hours) for São Paulo, Sydney, Stockholm, and Amsterdam, respectively. The travel time disparity, as quantified by the travel time ratio R (PT travel time divided by the car travel time), varies widely during an average weekday, by location and time of day. A systematic comparison between these two modes shows that the average travel time disparity is surprisingly similar across cities: R<1 for travel distances less than 3 km, then increases rapidly but quickly stabilises at around 2. This study contributes to providing a more realistic performance evaluation that helps future studies further explore what city characteristics as well as urban and transport policies make public transport more attractive, and to create a more sustainable future for cities.
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| 12. |
- Liao, Yuan, 1991, et al.
(författare)
-
Feasibility of estimating travel demand using geolocations of social media data
- 2022
-
Ingår i: Transportation. - : Springer Science and Business Media LLC. - 0049-4488 .- 1572-9435. ; 49:1, s. 137-161
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Tidskriftsartikel (refereegranskat)abstract
- Travel demand estimation, as represented by an origin–destination (OD) matrix, is essential for urban planning and management. Compared to data typically used in travel demand estimation, the key strengths of social media data are that they are low-cost, abundant, available in real-time, and free of geographical partition. However, the data also have significant limitations: population and behavioural biases, and lack of important information such as trip purpose and social demographics. This study systematically explores the feasibility of using geolocations of Twitter data for travel demand estimation by examining the effects of data sparsity, spatial scale, sampling methods, and sample size. We show that Twitter data are suitable for modelling the overall travel demand for an average weekday but not for commuting travel demand, due to the low reliability of identifying home and workplace. Collecting more detailed, long-term individual data from user timelines for a small number of individuals produces more accurate results than short-term data for a much larger population within a region. We developed a novel approach using geotagged tweets as attraction generators as opposed to the commonly adopted trip generators. This significantly increases usable data, resulting in better representation of travel demand. This study demonstrates that Twitter can be a viable option for estimating travel demand, though careful consideration must be given to sampling method, estimation model, and sample size.
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| 13. |
- Liao, Yuan, 1991, et al.
(författare)
-
From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data
- 2019
-
Ingår i: EPJ Data Science. - : Springer Science and Business Media LLC. - 2193-1127. ; 8:1
-
Tidskriftsartikel (refereegranskat)abstract
- This paper examines the population heterogeneity of travel behaviours from a combined perspective of individual actors and collective behaviours. We use a social media dataset of 652,945 geotagged tweets generated by 2,933 Swedish Twitter users covering an average time span of 3.6 years. No explicit geographical boundaries, such as national borders or administrative boundaries, are applied to the data. We use spatial features, such as geographical characteristics and network properties, and apply a clustering technique to reveal the heterogeneity of geotagged activity patterns. We find four distinct groups of travellers: local explorers (78.0%), local returners (14.4%), global explorers (7.3%), and global returners (0.3%). These groups exhibit distinct mobility characteristics, such as trip distance, diffusion process, percentage of domestic trips, visiting frequency of the most-visited locations, and total number of geotagged locations. Geotagged social media data are gradually being incorporated into travel behaviour studies as user-contributed data sources. While such data have many advantages, including easy access and the flexibility to capture movements across multiple scales (individual, city, country, and globe), more attention is still needed on data validation and identifying potential biases associated with these data. We validate against the data from a household travel survey and find that despite good agreement of trip distances (one-day and long-distance trips), we also find some differences in home location and the frequency of international trips, possibly due to population bias and behaviour distortion in Twitter data. Future work includes identifying and removing additional biases so that results from geotagged activity patterns may be generalised to human mobility patterns. This study explores the heterogeneity of behavioural groups and their spatial mobility including travel and day-to-day displacement. The findings of this paper could be relevant for disease prediction, transport modelling, and the broader social sciences.
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| 14. |
- Liao, Yuan, 1991, et al.
(författare)
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Impacts of charging behavior on BEV charging infrastructure needs and energy use
- 2023
-
Ingår i: Transportation Research Part D: Transport and Environment. - : Elsevier BV. - 1361-9209. ; 116
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Tidskriftsartikel (refereegranskat)abstract
- Battery electric vehicles (BEVs) are vital in the sustainable future of transport systems. Increased BEV adoption makes the realistic assessment of charging infrastructure demand critical. The current literature on charging infrastructure often uses outdated charging behavior assumptions such as universal access to home chargers and the "Liquid-fuel" mental model. We simulate charging infrastructure needs using a large-scale agent-based simulation of Sweden with detailed individual characteristics, including dwelling types and activity patterns. The two state-of-art archetypes of charging behaviors, "Plan-ahead" and "Event-triggered," mirror the current infrastructure built-up, suggesting 2.3-4.5 times more public chargers per BEV than the "Liquid-fuel" mental model. We also estimate roughly 30-150 BEVs served by a slow charger may be needed for non-home residential overnight charging.
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| 15. |
- Liao, Yuan, 1991, et al.
(författare)
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Predictability in Human Mobility based on Geographical-boundary-free and Long-time Social Media Data
- 2018
-
Ingår i: 2018 21st International Conference on Intelligent Transportation Systems (ITSC). - 2153-0017. - 9781728103211 ; 2018-November, s. 2068-2073
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Konferensbidrag (refereegranskat)abstract
- Understanding of predictability in human mobility benefits a broad spectrum such as urban planning and traffic forecasting. In human mobility studies, geotagged social media data are being gradually accepted as a user-contributed data source. It remains unclear to what extent we can use geotagged social media data to predict individual mobility. In the present study, a dataset is collected and applied which includes 652,945 geotagged tweets generated by 2,933 Swedish users covering time spans of more than one year (3.6 years on average). Based on such a dataset, human mobility predictability has been explored from three aspects: 1) time history of mobility range indicating how people diffuse in space, 2) entropy and the corresponding predictability of mobility, and 3) the limits of predictability dependent on geographical boundaries and mobility range. This study reveals a dataset that captures Twitter users' mobility where they routinely visit a couple of regions at most of the time and occasionally explore new regions. A 70% potential predictability is obtained by measuring the entropy of each individual's geotagged activity trajectory using a half-day time interval. The predictability's dependence on mobility range is prolonged when the observation of mobility is geographical-boundary-free which also decreases predictability.
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| 16. |
- Liao, Yuan, 1991
(författare)
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Ride-sourcing compared to its public-transit alternative using big trip data
- 2021
-
Ingår i: Journal of Transport Geography. - : Elsevier BV. - 0966-6923. ; 95
-
Tidskriftsartikel (refereegranskat)abstract
- Ride-sourcing risks increasing GHG emissions by replacing public transit (PT) for some trips therefore, understanding the relation of ride-sourcing to PT in urban mobility is crucial. This study explores the competition between ride-sourcing and PT through the lens of big data analysis. This research uses 4.3 million ride-sourcing trip records collected from Chengdu, China over a month, dividing these into two categories, transit-competing (48.2%) and non-transit-competing (51.8%). Here, a ride-sourcing trip is labelled transit-competing if and only if it occurs during the day and there is a PT alternative such that the walking distance associated with it is less than 800 m for access and egress alike. We construct a glass-box model to characterise the two ride-sourcing trip categories based on trip attributes and the built environment from the enriched trip data. This study provides a good overview of not only the main factors affecting the relationship between ride-sourcing and PT, but also the interactions between those factors. The built environment, as characterised by points of interest (POIs) and transit-stop density, is the most important aspect followed by travel time, number of transfers, weather, and a series of interactions between them. Competition is more likely to arise if: (1) the travel time by ride-sourcing <15 min or the travel time by PT is disproportionately longer than ride-sourcing; (2) the PT alternative requires multiple transfers, especially for the trips happening within the transition area between the central city and the outskirts; (3) the weather is good; (4) land use is high-density and high-diversity; (5) transit access is good, especially for the areas featuring a large number of business and much real estate. Based on the main findings, we discuss a few recommendations for transport planning and policymaking.
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| 17. |
- Liao, Yuan, 1991, et al.
(författare)
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Understanding Driver Response Patterns to Mental Workload Increase in Typical Driving Scenarios
- 2018
-
Ingår i: IEEE Access. - 2169-3536 .- 2169-3536. ; 6, s. 35890-35900
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Tidskriftsartikel (refereegranskat)abstract
- As vehicles become more complex and traffic increases, the associated mental workload of driving should increase, potentially compromising driving safety. As mental workload increases (as measured by the detection response time task), does how people drive (as assessed by driving performance and eye fixations) change? How does driving experience impact on such response patterns? To address those questions, data were collected in a motion-based driving simulator. Two driving scenarios were examined, a stop-controlled intersection (high workload — 16 participants, 320 trials) and speed-limited highway (low workload — 11 participants, 264 trials). In each scenario, in half of the trials, the participants were required to complete or not to complete a distracting secondary task. Hierarchical cluster analysis was used to identify driver response patterns. For highway driving, they are: (1) increased eye fixation variability and unchanged driving performance, and (2) unchanged fixation variability and increased mean speed. For intersection driving, they are: (1) increased and (2) decreased fixation variability both with decreased speed (mean and variance), and (3) increased fixation variability with increased speed. Eye fixation variability was more strongly associated with increased mental workload than other driving performance statistics. Furthermore, in contrast to prior research, changes in driving performance and eye fixations were not necessarily correlated with each other as mental workload increased. Novice drivers exhibit higher gaze variability, and they are more prone to maintain vehicle control than experienced drivers.
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| 18. |
- Liao, Yuan, 1991
(författare)
-
Understanding Human Mobility with Emerging Data Sources: Validation, spatiotemporal patterns, and transport modal disparity
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Human mobility refers to the geographic displacement of human beings, seen as individuals or groups, in space and time. The understanding of mobility has broad relevance, e.g., how fast epidemics spread globally. After 2030, transport is likely to become the sector with the highest emissions in the 2°C scenario. Better informed policy-making requires up-to-date empirical mobility data with good quality. However, the conventional methods are limited when dealing with new challenges. The prevalence of digital technologies enables a large-scale collection of human mobility traces, through social media data and GPS-enabled devices etc, which contribute significantly to the understanding of human mobility. However, their potentials for the further application are not fully exploited. This thesis uses emerging data sources, particularly Twitter data, to enhance the understanding of mobility and apply the obtained knowledge in the field of transport. The thesis answers three questions: Is Twitter a feasible data source to represent individual and population mobility? How are Twitter data used to reveal the spatiotemporal dynamics of mobility? How do Twitter data contribute to depicting the modal disparity of travel time by car vs public transit? In answering these questions, the methodological contribution of this thesis lies in the applied side of data science. Using geotagged Twitter data, mobility is firstly described by abstract metrics and physical models; in Paper A to reveal the population heterogeneity of mobility patterns using data mining techniques; and in Paper B to estimate travel demand with a novel approach to address the sparsity issue of Twitter data. In Paper C, GIS techniques are applied to combine the travel demand as revealed by Twitter data and the transportation network to give a more realistic picture of the modal disparity in travel time between car and public transit in four cities in different countries at a high spatial and temporal granularity. The validation of using Twitter data in mobility study contributes to better utilisation of this low-cost mobility data source. Compared with a static picture obtained by conventional data sources, the dynamics introduced by social media data among others contribute to better-informed policymaking and transport planning.
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| 19. |
- Liao, Yuan, 1991
(författare)
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Understanding Mobility and Transport Modal Disparities Using Emerging Data Sources: Modelling Potentials and Limitations
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Transportation presents a major challenge to curb climate change due in part to its ever-increasing travel demand. Better informed policy-making requires up-to-date empirical mobility data to model viable mitigation options for reducing emissions from the transport sector. On the one hand, the prevalence of digital technologies enables a large-scale collection of human mobility traces, providing big potentials for improving the understanding of mobility patterns and transport modal disparities. On the other hand, the advancement in data science has allowed us to continue pushing the boundary of the potentials and limitations, for new uses of big data in transport. This thesis uses emerging data sources, including Twitter data, traffic data, OpenStreetMap (OSM), and trip data from new transport modes, to enhance the understanding of mobility and transport modal disparities, e.g., how car and public transit support mobility differently. Specifically, this thesis aims to answer two research questions: (1) What are the potentials and limitations of using these emerging data sources for modelling mobility? (2) How can these new data sources be properly modelled for characterising transport modal disparities? Papers I-III model mobility mainly using geotagged social media data, and reveal the potentials and limitations of this data source by validating against established sources (Q1). Papers IV-V combine multiple data sources to characterise transport modal disparities (Q2) which further demonstrate the modelling potentials of the emerging data sources (Q1). Despite a biased population representation and low and irregular sampling of the actual mobility, the geolocations of Twitter data can be used in models to produce good agreements with the other data sources on the fundamental characteristics of individual and population mobility. However, its feasibility for estimating travel demand depends on spatial scale, sparsity, sampling method, and sample size. To extend the use of social media data, this thesis develops two novel approaches to address the sparsity issue: (1) An individual-based mobility model that fills the gaps in the sparse mobility traces for synthetic travel demand; (2) A population-based model that uses Twitter geolocations as attractions instead of trips for estimating the flows of people between regions. This thesis also presents two reproducible data fusion frameworks for characterising transport modal disparities. They demonstrate the power of combining different data sources to gain new insights into the spatiotemporal patterns of travel time disparities between car and public transit, and the competition between ride-sourcing and public transport.
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| 20. |
- Tozluoglu, Çaglar, 1988, et al.
(författare)
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A synthetic population of Sweden: datasets of agents, households, and activity-travel patterns
- 2023
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Ingår i: Data in Brief. - 2352-3409. ; 48
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Tidskriftsartikel (refereegranskat)abstract
- A synthetic population is a simplified microscopic representation of an actual population. Statistically representative at the population level, it provides valuable inputs to simulation models (especially agent-based models) in research areas such as transportation, land use, economics, and epidemiology. This article describes the datasets from the Synthetic Sweden Mobility (SySMo) model using the state-of-art methodology, including machine learning (ML), iterative proportional fitting (IPF), and probabilistic sampling. The model provides a synthetic replica of over 10 million Swedish individuals (i.e., agents), their household characteristics, and activity-travel plans. This paper briefly explains the methodology for the three datasets: Person, Households, and Activity-travel patterns. Each agent contains socio-demographic attributes, such as age, gender, civil status, residential zone, personal income, car ownership, employment, etc. Each agent also has a household and corresponding attributes such as household size, number of children ≤ 6 years old, etc. These characteristics are the basis for the agents’ daily activity-travel schedule, including type of activity, start-end time, duration, sequence, the location of each activity, and the travel mode between activities.
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| 21. |
- Tozluoglu, Çaglar, 1988, et al.
(författare)
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Synthetic Sweden Mobility (SySMo) Model Documentation
- 2022
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- This document describes a decision support framework using a combination of several state-of-the-art computing tools and techniques in synthetic information systems, and large-scale agent-based simulations. In this work, we create a synthetic population of Sweden and their mobility patterns that are composed of three major components: population synthesis, activity generation, and location assignment. The document describes the model structure, assumptions, and validation of results.
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| 22. |
- Wang, Minjuan, 1982, et al.
(författare)
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How drivers respond to visual vs. auditory information in advisory traffic information systems
- 2020
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Ingår i: Behaviour and Information Technology. - 1362-3001 .- 0144-929X. ; 39:12, s. 1308-1319
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Tidskriftsartikel (refereegranskat)abstract
- To date, many efforts have been made to explore how to support driver's decision-making process with advisory information. Previous studies mainly focus on a single modality, e.g. the visual, auditory or haptic modality. In contrast, this study compares data from two simulator studies with 50 participants in total, where the visual vs. the auditory modality was used to present the same type of advisory traffic information under the same driving scenarios. Hereby we compare the effect of these two modalities on drivers' responses and driving performance. Our findings indicate that modality influences the drivers' behaviour patterns significantly. Visual information helps drivers to drive more accurately and efficiently, whereas auditory information supports quicker responses. This suggests that there are potential benefits in applying both modalities in tandem, as they complement each other. Correspondingly, we present several design recommendations on Advisory Traffic Information Systems.
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| 23. |
- Yan, Haoyang, et al.
(författare)
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Improving multi-modal transportation recommendation systems through contrastive De-biased heterogenous graph neural networks
- 2024
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Ingår i: Transportation Research Part C. - : Elsevier Ltd. - 0968-090X .- 1879-2359. ; 164
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Tidskriftsartikel (refereegranskat)abstract
- Conventional uni-modal transportation recommendation systems focused on single modes of transportation are limited in providing satisfactory solutions since passengers often undertake journeys involving multiple modes. Multi-modal transportation recommendation systems are becoming increasingly popular within navigation applications. However, these systems face challenges from biased raw data, data sparsity and long-tail distribution, as well as complexities in representing large-scale graph structures, which collectively hinder their optimal performance. This study introduces a novel approach for enhancing multi-modal transportation recommendation systems: the Contrastive De-biased Heterogeneous Graph Neural Network (CDHGNN). By integrating contrastive learning, the model generates augmented samples to mitigate bias and overcome the data-skewing problem. The heterogeneous graph neural network adaptively captures temporal and spatial patterns among users and locations, as well as spatial adjacency and attribute relations, leading to enhanced representations of nodes, and consequently, improved model performance. The proposed method was evaluated using real-world data from over 300,000 users’ records in Beijing over two months in 2018. The extensive experiments demonstrate that the approach outperforms four contemporary state-of-the-art methods. The results underscore the potential of the CDHGNN in large-scale city-level problems in practical applications, revealing a promising advancement for multi-modal transportation recommendation systems.
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| 24. |
- Yeh, Sonia, 1973, et al.
(författare)
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Improving future travel demand projections: a pathway with an open science interdisciplinary approach
- 2022
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Ingår i: Progress in Energy. - : IOP Publishing. - 2516-1083. ; 4:4
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Tidskriftsartikel (refereegranskat)abstract
- Transport accounts for 24% of global CO2 emissions from fossil fuels. Governments face challenges in developing feasible and equitable mitigation strategies to reduce energy consumption and manage the transition to low-carbon transport systems. To meet the local and global transport emission reduction targets, policymakers need more realistic/sophisticated future projections of transport demand to better understand the speed and depth of the actions required to mitigate greenhouse gas emissions. In this paper, we argue that the lack of access to high-quality data on the current and historical travel demand and interdisciplinary research hinders transport planning and sustainable transitions toward low-carbon transport futures. We call for a greater interdisciplinary collaboration agenda across open data, data science, behaviour modelling, and policy analysis. These advancemets can reduce some of the major uncertainties and contribute to evidence-based solutions toward improving the sustainability performance of future transport systems. The paper also points to some needed efforts and directions to provide robust insights to policymakers. We provide examples of how these efforts could benefit from the International Transport Energy Modeling Open Data project and open science interdisciplinary collaborations.
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