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| 2. |
- Allström, Andreas, 1978-, et al.
(författare)
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A hybrid approach for short-term traffic state and travel time prediction on highways
- 2016
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Ingår i: TRB 95th annual meeting compendium of papers.
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Konferensbidrag (refereegranskat)abstract
- Traffic management and traffic information are essential in urban areas, and require a good knowledge about both the current and the future traffic state. Both parametric and non-parametric traffic state prediction techniques have previously been developed, with different advantages and shortcomings. While non-parametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during non-recurring traffic conditions such as incidents and events. Hybrid approaches, combining the two prediction paradigms have previously been proposed by using non-parametric methods for predicting boundary conditions used in a parametric method. In this paper we instead combine parametric and non-parametric traffic state prediction techniques through assimilation in an Ensemble Kalman filter. As non-parametric prediction method a neural network method is adopted, and the parametric prediction is carried out using a cell transmission model with velocity as state. The results show that our hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 minutes into the future, using a prediction horizon of up to 50 minutes ahead in time to allow the journey to be completed.
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| 3. |
- Allström, Andreas, et al.
(författare)
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Calibration Framework based on Bluetooth Sensors for Traffic State Estimation Using a Velocity based Cell Transmission Model
- 2014
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Ingår i: Transportation Research Procedia. - : Elsevier. - 2352-1465. ; 3, s. 972-981
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Tidskriftsartikel (refereegranskat)abstract
- The velocity based cell transmission model (CTM-v) is a discrete time dynamical model that mimics the evolution of the traffic velocity field on highways. In this paper the CTM-v model is used together with an ensemble Kalman filter (EnKF) for the purpose of velocity sensor data assimilation. We present a calibration framework for the CTM-v and EnKF. The framework consists of two separate phases. The first phase is the calibration of the parameters of the fundamental diagram and the second phase is the calibration of demand and filter parameters. Results from the calibrated model are presented for a highway stretch north of Stockholm, Sweden.
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| 4. |
- Allström, Andreas, 1978-, et al.
(författare)
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Evaluation of travel time estimation based on LWR-v and CTM-v : A case study in Stockholm
- 2012
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Ingår i: 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2012. - Piscataway, N.J, USA : IEEE. - 9781467330640 - 9781467330626 ; , s. 1644-1649
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Konferensbidrag (refereegranskat)abstract
- Real-time estimations of current and future traffic states are an essential part of traffic management and traffic information systems. Within the Mobile Millennium project considerable effort has been invested in the research and development of a real-time estimation system that can fuse several sources of data collected in California. During the past year this system has been adapted to also handle traffic data collected in Stockholm. This paper provides an overview of the model used for highways and presents results from an initial evaluation of the system. As part of the evaluation process, GPS data collected in an earlier field-test and estimations generated by the existing system used by the TMC in Stockholm, are compared with the estimations generated by the Mobile Millennium system. Given that the Mobile Millennium Stockholm system has not undergone any calibration, the results from the evaluation are considered promising. The estimated travel times correspond well to those measured in the field test. Furthermore, the estimations generated by the Mobile Millennium system can be regarded as superior to those of existing traffic management system in Stockholm. The highway model was found to perform well even with a reduction in the number of sensors providing data. The findings of this study indicate the robustness of the Mobile Millennium system and demonstrate how the system can be migrated to other geographical areas with similar sources of available data.
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| 5. |
- Allström, Andreas
(författare)
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Highway Traffic State Estimation and Short-term Prediction
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Traffic congestion is increasing in almost all large cities, leading to a number of negative effects such as pollution and delays. However, building new roads is not a feasible solution. Instead, the use of the existing road network has to be optimized, together with a shift towards more sustainable transport modes. In order to achieve this there are several challenges that needs to be addressed. One challenge is the ability to provide accurate information about the current and future traffic state. This information is an essential input to the traffic management center and can be used to influence the choices made by the travelers. Accurate information about the traffic state on highways, where the potential to manage and control the traffic in general is very high, would be of great significance for the traffic managers. It would help the traffic managers to take action before the system reaches congestion and limit the effects of it. At the same time, the collection of traffic data is slowly shifting from fixed sensors to more probe based data collection. This requires an adaptation and further development of the traditional traffic models in order for them to handle and take advantage of the characteristics of all types of data, not just data from the traditionally used fixed sensors.The objective of this thesis is to contribute to the development and implementation of a model for estimation and prediction of the current and future traffic state and to facilitate an adaptation of the model to the conditions of the highway in Stockholm. The model used is a version of the Cell Transmission Model (CTM-v) where the velocity is used as the state variable. Thus, together with an Ensemble Kalman Filter (EnKF) it can be used to fuse different types of point speed measurements. The model is developed to run in real-time for a large network. Furthermore, a two-stage process used to calibrate the model is implemented. The results from the calibration and validation show that once the model is calibrated, the estimated travel times corresponds well with the ground truth travel times collected from Bluetooth sensors.In order to produce accurate short-term predictions for various networks and conditions it is vital to combine different methods. We have implemented and evaluated a hybrid prediction approach that assimilates parametric and non-parametric short-term traffic state prediction. To predict mainline sensor data we use a neural network, while the CTM-v is ran forward in time in order to predict future traffic states. The results show that both the hybrid approach and the CTM-v prediction without the additional predicted mainline sensor data is superior to a naïve prediction method for longer prediction horizons.
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| 6. |
- Allström, Andreas, 1978-, et al.
(författare)
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Hybrid Approach for Short-Term Traffic State and Travel Time Prediction on Highways
- 2016
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Ingår i: Transportation Research Record. - Washington, DC, USA : The National Academies of Sciences, Engineering, and Medicine. - 0361-1981 .- 2169-4052. ; 2554, s. 60-68
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Tidskriftsartikel (refereegranskat)abstract
- Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches combine the two prediction paradigms by using nonparametric methods for predicting boundary conditions used in a parametric method. In this paper, parametric and nonparametric traffic state prediction techniques are instead combined through assimilation in an ensemble Kalman filter. For nonparametric prediction, a neural network method is adopted; the parametric prediction is carried out with a cell transmission model with velocity as state. The results show that the hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 min into the future, with a prediction horizon of up to 50 min ahead in time to allow the journey to be completed
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| 7. |
- Allström, Andreas, et al.
(författare)
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Traffic management for smart cities
- 2016
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Ingår i: Designing, developing, and facilitating smart cities. - Switzerland : Springer. - 9783319449227 - 9783319449241 ; , s. 211-240
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Smart cities, participatory sensing as well as location data available in communication systems and social networks generates a vast amount of heterogeneous mobility data that can be used for traffic management. This chapter gives an overview of the different data sources and their characteristics and describes a framework for utilizing the various sources efficiently in the context of traffic management. Furthermore, different types of traffic models and algorithms are related to both the different data sources as well as some key functionalities of active traffic management, for example short-term prediction and control.
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| 8. |
- Almlöf, Erik, 1985-
(författare)
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Exploring societal impacts of self-driving public transport using four-step transport models
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- During the last decade, self-driving technology has become increasingly visible in the news, with the vision that people would enter vehicles that drive themselves, and that people could instead rest, read the newspaper, or have a meeting. However, these visions have mainly focused on the potential for car usage, even though public transport could benefit greatly from self-driving technology. For bus traffic, the bus driver accounts for half of the cost of driving, and savings on personnel costs could, for example, be reinvested in expanded public transport service or used to lower taxes.At the same time, more research has shown potential problems linked to self-driving technology, for example that more comfortable driving would lead to more traffic, which in turn would lead to increased emissions, higher noise levels in cities or further focus on car-centric infrastructure. For public transport, the driver's role in creating safety and acting as problem solvers has also been emphasized - who should I ask for directions if there is no knowledgeable driver on board?Various methods have previously been used to explore the social effects of self-driving technology and in this dissertation I have used so-called "four-stage models", more specifically the Swedish transport model Sampers. Four-stage models have been used for 50 years to evaluate effects on the transport system from e.g. infrastructure changes, but these models face new challenges, handling vehicles that drive by themselves. In my research, I have adjusted the model to simulate self-driving technology and investigated what effects this has on, for example, traffic volumes and emissions.In the three articles that are part of the dissertation, I have four main conclusions:Self-driving technology can mean large savings in costs for public transport, primarily for bus traffic but also to some extent for rail traffic. In addition, a smoother driving behaviour would mean more comfortable travel, which would increase the attractiveness of public transport. In addition, public transport not limited by, for example, driver schedules or current commercial conditions, could develop new types of services, such as on-demand public transport.Four-stage models have previously been used to model the transport system and have been shown to have good results, at least at an overall level. Within my research, I have made some adaptations of these models to mimic self-driving technology, but the models in their current form cannot consider, for example, vehicle sharing.It is important to point out that bus and train drivers currently perform many tasks that are not directly related to the driving of the vehicle, such as answering questions, maintaining social order among passengers and taking care of faults that occur during the trip. Today, self-driving technology cannot fulfil these roles.Self-driving technology for public transport would affect people's accessibility, driving style for vehicles, safety on board, how we plan traffic and the people who currently work as drivers. In fact, a multitude of societal effects have been identified, affecting all areas of transport. In addition, the effects are generally not similar across geographies, time units or for different actors, which further emphasizes that the total effect is not easy to summarize.
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| 9. |
- Andersson, Angelica
(författare)
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Mode choice modelling of long-distance passenger transport based on mobile phone network data
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Reliable forecasting models are needed to achieve the climate related goals in the face of increasing transport demand. Such models can predict the long-term behavioural response to policy interventions, including infrastructure investments, and thus provide valuable pre-dictions for decision makers. Contemporary forecasting models are mainly based on national travel surveys. Unfortunately, the response rates of such surveys have steadily declined, implying that the respondents become less representative of the whole population. A particular weakness is that it is likely that respondents with a high valuation of time are less willing to respond to surveys (because they have less time available for such), and therefore there is a high chance that they are underrepresented among the respondents. The valuation of time plays an important role for the cost benefit analyses of public policies including transport investments, and there is no reliable way of controlling for this uneven sampling of time preferences. Fortunately, there is simultaneously an increase in the number of signals sent between mobile phones and network antennae, and research has now reached the point where it is possible to determine not only the travel destination but also the travel mode based on mobile phone network antennae connections. The aim of this thesis is to investigate if and how mobile phone network data can be used to estimate transportation mode choice demand models that can be used for forecasting and planning. Key challenges with using this data source in the context of mode choice models are identified and met. The identified challenges include uncertainty in the choice variable, the difficulty to distinguish car and bus trips, and the lack of information about the trip purpose. In the first paper we propose three possible model formulations and analyse how the uncertainty in the choice outcome variable would play a role in the different model formulations. We also conclude that it is indeed possible to estimate mode choice demand models based on mobile phone network data, with good results in terms of behavioural interpretability and significance. In the second paper we estimate models using a nested logit structure to account for the difficulty in separating bus and car, and a latent class model specification to meet the challenge of having an unknown trip purpose.
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| 10. |
- Andersson, Angelica, 1990-
(författare)
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Modelling long-distance travel demand by combining mobile phone and survey data
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Forecasts of the demand for long-distance travel are a key component enabling the calculation of social costs and benefits of policy actions such as infrastructure investments. Traditionally, such forecasting models have been based on travel survey data. However, response rates to travel surveys have been in decline for decades, calling into question whether the sample of respondents is really representative of the full population. As such, there is a need to explore alternative data sources. One promising alternative is mobile phone network data, which is collected without the need of active participation from the traveller. However, mobile phone network data in this thesis lacks trip and traveller specific information such as trip purpose, socio-economic information, travel party size and mode. Furthermore, it is difficult to distinguish between bus and car trips even at a later stage of data processing, as the two modes share the same infrastructure. The objective of this thesis is to investigate the use of mobile phone network data for long-distance mode choice modelling. More specifically, we investigate the specific aspects of mobile phone network data as a source of mode choice travel information in the first research paper of this thesis, how uncertainties connected to the identification of the used mode matter, and how it can be handled in the model. In the second research paper of this thesis, a full-scale Multinomial Logit mode choice model is implemented and evaluated, including the development of how to handle mobile phone network data-specific challenges in the dataset of this thesis, such as the lack of distinction between bus and car trips and the lack of trip purpose information. Once this full-scale mode choice model based only on mobile phone network data has been evaluated, a method for combining mobile phone network data with survey data is proposed in the third research paper of this thesis, and the joint model is compared to the mobile phone network data model in terms of behavioural credibility. Finally, it is investigated whether machine learning can be useful in modelling mode choices using the two data sources in the fourth research paper of this thesis. From the results of the papers included in this thesis, it is clear that it is possible to model mode choice based only on mobile phone network data, but that it is preferable to combine mobile phone network data with survey data, rather than to use any one data source separately. Either Multinomial Logit (MNL) models or Artificial Neural Networks (ANNs) can be used to model mode choices based on the two data sources. However, if ANN is selected for mode choice modelling, it is advisable to formulate the network based on the transport mode choice specific principles developed in the last paper of this thesis.
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