Search

Efficient traffic management requires an understanding of mobility patterns in the road network, where one important component is route choice. This study aims to analyze how route choice models can be adapted to efficient urban traffic management and intelligent transport systems (ITS), by constructing route sets and attributes from GPS and network data. With a route choice model that is responsive to traveltime changes in the network, travel behavior during incidents can be predicted to evaluate traffic management policies, such as traveler information and traffic control. The dataset consists of about 400,000 vehicle trips and is divided into a training dataset and a test dataset. The two datasets are compared, and the experiments show that the routes used are similar. Discrete route choice models are estimated with one data-driven path identification approach (DDPI) and one where the data-driven path set is augmented with routes from a network-based shortest path generation with link penalty (NBPA). The result suggests that the traveltime has a larger impact on the route choice when the model is trained on the NBPA route set and that the route's simplicity, length, and traveltime are important attributes for the route choice, which are useful insights in a traffic management context., Funding: This work was supported by the Swedish Transport Administration (Trafikverket) via the Centre for Traffic Research (CTR) [grant number TRV 2020/118663].

The gradual deployment of automated vehicles on the existing road network will lead to a long transition period in which vehicles at different driving automation levels and capabilities will share the road with human driven vehicles, resulting into what is known as mixed traffic. Whether our road infrastructure is ready to safely and efficiently accommodate this mixed traffic remains a knowledge gap. Microscopic traffic simulation provides a proactive approach for assessing these implications. However, differences in assumptions regarding modeling automated driving in current simulation studies, and the use of different terminology make it difficult to compare the results of these studies. Therefore, the aim of this study is to specify the aspects to consider for modeling automated driving in microscopic traffic simulations using harmonized concepts, to investigate how both empirical studies and microscopic traffic simulation studies on automated driving have considered the proposed aspects, and to identify the state of the practice and the research needs to further improve the modeling of automated driving. Six important aspects were identified: the role of authorities, the role of users, the vehicle system, the perception of surroundings based on the vehicle’s sensors, the vehicle connectivity features, and the role of the infrastructure both physical and digital. The research gaps and research directions in relation to these aspects are identified and proposed, these might bring great benefits for the development of more accurate and realistic modeling of automated driving in microscopic traffic simulations., Funding agencies: Applied and Technical Sciences (TTW), a subdomain of the Dutch Institute for Scientific Research (NWO) through the Project Safe and Efficient Operation of Automated and Human-Driven Vehicles in Mixed Traffic (SAMEN) (Grant Number: 17187)Swedish Transport Administration (Trafikverket) through the Project Simulation and Modeling of Automated Road Transport (SMART) (Grant Number: TRV 2019/27044)

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

The gradual deployment of automated vehicles on the existing road network will lead to a long transition period in which vehicles at different driving automation levels and capabilities will share the road with human driven vehicles, resulting into what is known as mixed traffic. Whether our road infrastructure is ready to safely and efficiently accommodate this mixed traffic remains a knowledge gap. Microscopic traffic simulation provides a proactive approach for assessing these implications. However, differences in assumptions regarding modeling automated driving in current simulation studies, and the use of different terminology make it difficult to compare the results of these studies. Therefore, the aim of this study is to specify the aspects to consider for modeling automated driving in microscopic traffic simulations using harmonized concepts, to investigate how both empirical studies and microscopic traffic simulation studies on automated driving have considered the proposed aspects, and to identify the state of the practice and the research needs to further improve the modeling of automated driving. Six important aspects were identified: the role of authorities, the role of users, the vehicle system, the perception of surroundings based on the vehicle’s sensors, the vehicle connectivity features, and the role of the infrastructure both physical and digital. The research gaps and research directions in relation to these aspects are identified and proposed, these might bring great benefits for the development of more accurate and realistic modeling of automated driving in microscopic traffic simulations., Funding agencies: Applied and Technical Sciences (TTW), a subdomain of the Dutch Institute for Scientific Research (NWO) through the Project Safe and Efficient Operation of Automated and Human-Driven Vehicles in Mixed Traffic (SAMEN) (Grant Number: 17187)Swedish Transport Administration (Trafikverket) through the Project Simulation and Modeling of Automated Road Transport (SMART) (Grant Number: TRV 2019/27044)

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

The gradual deployment of automated vehicles on the existing road network will lead to a long transition period in which vehicles at different driving automation levels and capabilities will share the road with human driven vehicles, resulting into what is known as mixed traffic. Whether our road infrastructure is ready to safely and efficiently accommodate this mixed traffic remains a knowledge gap. Microscopic traffic simulation provides a proactive approach for assessing these implications. However, differences in assumptions regarding modeling automated driving in current simulation studies, and the use of different terminology make it difficult to compare the results of these studies. Therefore, the aim of this study is to specify the aspects to consider for modeling automated driving in microscopic traffic simulations using harmonized concepts, to investigate how both empirical studies and microscopic traffic simulation studies on automated driving have considered the proposed aspects, and to identify the state of the practice and the research needs to further improve the modeling of automated driving. Six important aspects were identified: the role of authorities, the role of users, the vehicle system, the perception of surroundings based on the vehicle’s sensors, the vehicle connectivity features, and the role of the infrastructure both physical and digital. The research gaps and research directions in relation to these aspects are identified and proposed, these might bring great benefits for the development of more accurate and realistic modeling of automated driving in microscopic traffic simulations., Funding agencies: Applied and Technical Sciences (TTW), a subdomain of the Dutch Institute for Scientific Research (NWO) through the Project Safe and Efficient Operation of Automated and Human-Driven Vehicles in Mixed Traffic (SAMEN) (Grant Number: 17187)Swedish Transport Administration (Trafikverket) through the Project Simulation and Modeling of Automated Road Transport (SMART) (Grant Number: TRV 2019/27044)

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

The gradual deployment of automated vehicles on the existing road network will lead to a long transition period in which vehicles at different driving automation levels and capabilities will share the road with human driven vehicles, resulting into what is known as mixed traffic. Whether our road infrastructure is ready to safely and efficiently accommodate this mixed traffic remains a knowledge gap. Microscopic traffic simulation provides a proactive approach for assessing these implications. However, differences in assumptions regarding modeling automated driving in current simulation studies, and the use of different terminology make it difficult to compare the results of these studies. Therefore, the aim of this study is to specify the aspects to consider for modeling automated driving in microscopic traffic simulations using harmonized concepts, to investigate how both empirical studies and microscopic traffic simulation studies on automated driving have considered the proposed aspects, and to identify the state of the practice and the research needs to further improve the modeling of automated driving. Six important aspects were identified: the role of authorities, the role of users, the vehicle system, the perception of surroundings based on the vehicle’s sensors, the vehicle connectivity features, and the role of the infrastructure both physical and digital. The research gaps and research directions in relation to these aspects are identified and proposed, these might bring great benefits for the development of more accurate and realistic modeling of automated driving in microscopic traffic simulations., Funding agencies: Applied and Technical Sciences (TTW), a subdomain of the Dutch Institute for Scientific Research (NWO) through the Project Safe and Efficient Operation of Automated and Human-Driven Vehicles in Mixed Traffic (SAMEN) (Grant Number: 17187)Swedish Transport Administration (Trafikverket) through the Project Simulation and Modeling of Automated Road Transport (SMART) (Grant Number: TRV 2019/27044)

The gradual deployment of automated vehicles on the existing road network will lead to a long transition period in which vehicles at different driving automation levels and capabilities will share the road with human driven vehicles, resulting into what is known as mixed traffic. Whether our road infrastructure is ready to safely and efficiently accommodate this mixed traffic remains a knowledge gap. Microscopic traffic simulation provides a proactive approach for assessing these implications. However, differences in assumptions regarding modeling automated driving in current simulation studies, and the use of different terminology make it difficult to compare the results of these studies. Therefore, the aim of this study is to specify the aspects to consider for modeling automated driving in microscopic traffic simulations using harmonized concepts, to investigate how both empirical studies and microscopic traffic simulation studies on automated driving have considered the proposed aspects, and to identify the state of the practice and the research needs to further improve the modeling of automated driving. Six important aspects were identified: the role of authorities, the role of users, the vehicle system, the perception of surroundings based on the vehicle’s sensors, the vehicle connectivity features, and the role of the infrastructure both physical and digital. The research gaps and research directions in relation to these aspects are identified and proposed, these might bring great benefits for the development of more accurate and realistic modeling of automated driving in microscopic traffic simulations., Funding agencies: Applied and Technical Sciences (TTW), a subdomain of the Dutch Institute for Scientific Research (NWO) through the Project Safe and Efficient Operation of Automated and Human-Driven Vehicles in Mixed Traffic (SAMEN) (Grant Number: 17187)Swedish Transport Administration (Trafikverket) through the Project Simulation and Modeling of Automated Road Transport (SMART) (Grant Number: TRV 2019/27044)

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

Mixed traffic with automated and human-driven vehicles interacting with one another will soon become a common reality. Microscopic traffic simulation can preemptively help assess the impact on the traffic flow dynamics as long as the tools adequately capture the differences on how automated driving systems (ADSs) drive compared to humans. In this work a modeling approach that captures differences in perception performance is proposed. While human drivers perceive through their senses and cognitive processes, ADS perceive the driving context through on-board sensors, connectivity features and software. The perception performance is described in terms of accuracy, precision, detection range, and detection delay. The model for perception is implemented in SUMO and a simulation test in a platoon shows the acceleration response affected by up to 35% for perception errors of approximate to 10% which by extension will affect the traffic flow dynamics. The proposed modeling approach for perception contributes to the robustness of microscopic traffic simulation and the modeling of heterogeneous mixed traffic., Funding Agencies|Swedish Transport Administration via the Centre for Traffic Research (Trafikverket) [TRV 2022/8287]

Good knowledge of travel patterns is essential in transportation planning. Cellular network data as a large-scale passive data source provides billions of daily location updates allowing us to observe human mobility with all travel modes. However, many transport planning applications require an understanding of travel patterns separated by travel mode, requiring the classification of trips by travel mode. Most previous studies have used rule-based or geometric classification, which often fails when the routes for different modes are similar or supervised classification, requiring labelled training trips. Sufficient amounts of labelled training trips are unfortunately often unavailable in practice. We propose semi-supervised classification as a novel approach of classifying large sets of trips extracted from cellular network data in inter-city origin–destination pairs as either using road or rail. Our methods require no labelled trips which is an important advantage as labeled data is often not available in practice. We propose three methods which first label a small share of trips using geometric classification. We then use structures in a large set of unlabelled trips using a supervised classification method (geometric-labelling), iterative semi-supervised training (self-labelling) and by transferring information between origin–destination pairs (continuity-labelling). We apply the semi-supervised classification methods on a dataset of 9545 unlabelled trips in two inter-city origin–destination pairs. We find that the methods can identify structures in the cells used during trips in the unlabelled data corresponding to the available route alternatives. We validate the classification methods using a dataset of 255 manually labelled trips in the two origin–destination pairs. While geometric classification misclassifies 4.2% and 5.6% of the trips in the two origin–destination pairs, all trips can be classified correctly using semi-supervised classification.

Good knowledge of travel patterns is essential in transportation planning. Cellular network data as a large-scale passive data source provides billions of daily location updates allowing us to observe human mobility with all travel modes. However, many transport planning applications require an understanding of travel patterns separated by travel mode, requiring the classification of trips by travel mode. Most previous studies have used rule-based or geometric classification, which often fails when the routes for different modes are similar or supervised classification, requiring labelled training trips. Sufficient amounts of labelled training trips are unfortunately often unavailable in practice. We propose semi-supervised classification as a novel approach of classifying large sets of trips extracted from cellular network data in inter-city origin–destination pairs as either using road or rail. Our methods require no labelled trips which is an important advantage as labeled data is often not available in practice. We propose three methods which first label a small share of trips using geometric classification. We then use structures in a large set of unlabelled trips using a supervised classification method (geometric-labelling), iterative semi-supervised training (self-labelling) and by transferring information between origin–destination pairs (continuity-labelling). We apply the semi-supervised classification methods on a dataset of 9545 unlabelled trips in two inter-city origin–destination pairs. We find that the methods can identify structures in the cells used during trips in the unlabelled data corresponding to the available route alternatives. We validate the classification methods using a dataset of 255 manually labelled trips in the two origin–destination pairs. While geometric classification misclassifies 4.2% and 5.6% of the trips in the two origin–destination pairs, all trips can be classified correctly using semi-supervised classification.

Good knowledge of travel patterns is essential in transportation planning. Cellular network data as a large-scale passive data source provides billions of daily location updates allowing us to observe human mobility with all travel modes. However, many transport planning applications require an understanding of travel patterns separated by travel mode, requiring the classification of trips by travel mode. Most previous studies have used rule-based or geometric classification, which often fails when the routes for different modes are similar or supervised classification, requiring labelled training trips. Sufficient amounts of labelled training trips are unfortunately often unavailable in practice. We propose semi-supervised classification as a novel approach of classifying large sets of trips extracted from cellular network data in inter-city origin–destination pairs as either using road or rail. Our methods require no labelled trips which is an important advantage as labeled data is often not available in practice. We propose three methods which first label a small share of trips using geometric classification. We then use structures in a large set of unlabelled trips using a supervised classification method (geometric-labelling), iterative semi-supervised training (self-labelling) and by transferring information between origin–destination pairs (continuity-labelling). We apply the semi-supervised classification methods on a dataset of 9545 unlabelled trips in two inter-city origin–destination pairs. We find that the methods can identify structures in the cells used during trips in the unlabelled data corresponding to the available route alternatives. We validate the classification methods using a dataset of 255 manually labelled trips in the two origin–destination pairs. While geometric classification misclassifies 4.2% and 5.6% of the trips in the two origin–destination pairs, all trips can be classified correctly using semi-supervised classification.

Good knowledge of travel patterns is essential in transportation planning. Cellular network data as a large-scale passive data source provides billions of daily location updates allowing us to observe human mobility with all travel modes. However, many transport planning applications require an understanding of travel patterns separated by travel mode, requiring the classification of trips by travel mode. Most previous studies have used rule-based or geometric classification, which often fails when the routes for different modes are similar or supervised classification, requiring labelled training trips. Sufficient amounts of labelled training trips are unfortunately often unavailable in practice. We propose semi-supervised classification as a novel approach of classifying large sets of trips extracted from cellular network data in inter-city origin–destination pairs as either using road or rail. Our methods require no labelled trips which is an important advantage as labeled data is often not available in practice. We propose three methods which first label a small share of trips using geometric classification. We then use structures in a large set of unlabelled trips using a supervised classification method (geometric-labelling), iterative semi-supervised training (self-labelling) and by transferring information between origin–destination pairs (continuity-labelling). We apply the semi-supervised classification methods on a dataset of 9545 unlabelled trips in two inter-city origin–destination pairs. We find that the methods can identify structures in the cells used during trips in the unlabelled data corresponding to the available route alternatives. We validate the classification methods using a dataset of 255 manually labelled trips in the two origin–destination pairs. While geometric classification misclassifies 4.2% and 5.6% of the trips in the two origin–destination pairs, all trips can be classified correctly using semi-supervised classification.

Good knowledge of travel patterns is essential in transportation planning. Cellular network data as a large-scale passive data source provides billions of daily location updates allowing us to observe human mobility with all travel modes. However, many transport planning applications require an understanding of travel patterns separated by travel mode, requiring the classification of trips by travel mode. Most previous studies have used rule-based or geometric classification, which often fails when the routes for different modes are similar or supervised classification, requiring labelled training trips. Sufficient amounts of labelled training trips are unfortunately often unavailable in practice. We propose semi-supervised classification as a novel approach of classifying large sets of trips extracted from cellular network data in inter-city origin–destination pairs as either using road or rail. Our methods require no labelled trips which is an important advantage as labeled data is often not available in practice. We propose three methods which first label a small share of trips using geometric classification. We then use structures in a large set of unlabelled trips using a supervised classification method (geometric-labelling), iterative semi-supervised training (self-labelling) and by transferring information between origin–destination pairs (continuity-labelling). We apply the semi-supervised classification methods on a dataset of 9545 unlabelled trips in two inter-city origin–destination pairs. We find that the methods can identify structures in the cells used during trips in the unlabelled data corresponding to the available route alternatives. We validate the classification methods using a dataset of 255 manually labelled trips in the two origin–destination pairs. While geometric classification misclassifies 4.2% and 5.6% of the trips in the two origin–destination pairs, all trips can be classified correctly using semi-supervised classification.

Many applications in transport planning require an understanding of travel patterns separated by travel mode. To use cellular network data as observations of human mobility in these applications, classification by travel mode is needed. Existing classification methods for GPS-trajectories are often inefficient for cellular network data, which has lower resolution in space and time than GPS data. In this study, we compare three geometry-based mode classification methods and three supervised methods to classify trips extracted from cellular network data in intercity origin-destination pairs as either road or train. To understand the difficulty of the problem, we use a labeled dataset of 255 trips in two OD-pairs to train the supervised classification methods and to evaluate the classification performance. For an OD-pair where the road and train routes are not separated by more than four kilometers, the geometry-based methods classify 4.5% - 7.1% of the trips wrong, while two of the supervised methods can classify all trips correctly. Using a large-scale dataset of 29037 trips, we find that separation between classes is less evident than in the labeled dataset and show that the choice of classification methods impacts the aggregated modal split estimate.

Dynamic Network Loading (DNL) models are typically formulated as a system of differential equations where travel times, densities or any other variable that indicates congestion is endogenous. However, such endogeneities increase the complexity of the Dynamic Traffic Assignment (DTA) problem due to the interdependence of DNL, route choice and demand. In this paper, attempting to exploit the growing accessibility of traffic-related data, we suggest that congestion can be instead captured by exogenous variables, such as travel time observations. We propagate the traffic flow based on an exogenous travel time function, which has a piece-wise linear form. Given piece-wise stationary route flows, the piece-wise linear form of the travel time function allows us to use an efficient event-based modelling structure. Our Data-Driven Network Loading (DDNL) approach is developed in accordance with the theoretical DNL framework ensuring vehicle conservation and FIFO. The first simulation experiment-based results are encouraging, indicating that the DDNL can contribute to improving the efficiency of applications where the monitoring of historical network-wide flows is required. Abbreviations: DDNL - Data Driven Network Loading; DNL - Dynamic Network Loading; DTA - Dynamic Traffic Assignment; ITS - Intelligent Transportation Systems; OD - Origin Destination; TTF - Travel Time Function; LTT - Linear Travel Time; DL - Demand level, Funding Agencies|Swedish Energy AgencySwedish Energy Agency [46963-1]

Dynamic Network Loading (DNL) models are typically formulated as a system of differential equations where travel times, densities or any other variable that indicates congestion is endogenous. However, such endogeneities increase the complexity of the Dynamic Traffic Assignment (DTA) problem due to the interdependence of DNL, route choice and demand. In this paper, attempting to exploit the growing accessibility of traffic-related data, we suggest that congestion can be instead captured by exogenous variables, such as travel time observations. We propagate the traffic flow based on an exogenous travel time function, which has a piece-wise linear form. Given piece-wise stationary route flows, the piece-wise linear form of the travel time function allows us to use an efficient event-based modelling structure. Our Data-Driven Network Loading (DDNL) approach is developed in accordance with the theoretical DNL framework ensuring vehicle conservation and FIFO. The first simulation experiment-based results are encouraging, indicating that the DDNL can contribute to improving the efficiency of applications where the monitoring of historical network-wide flows is required. Abbreviations: DDNL - Data Driven Network Loading; DNL - Dynamic Network Loading; DTA - Dynamic Traffic Assignment; ITS - Intelligent Transportation Systems; OD - Origin Destination; TTF - Travel Time Function; LTT - Linear Travel Time; DL - Demand level, Funding Agencies|Swedish Energy AgencySwedish Energy Agency [46963-1]

Many applications in transport planning require an understanding of travel patterns separated by travel mode. To use cellular network data as observations of human mobility in these applications, classification by travel mode is needed. Existing classification methods for GPS-trajectories are often inefficient for cellular network data, which has lower resolution in space and time than GPS data. In this study, we compare three geometry-based mode classification methods and three supervised methods to classify trips extracted from cellular network data in intercity origin-destination pairs as either road or train. To understand the difficulty of the problem, we use a labeled dataset of 255 trips in two OD-pairs to train the supervised classification methods and to evaluate the classification performance. For an OD-pair where the road and train routes are not separated by more than four kilometers, the geometry-based methods classify 4.5% - 7.1% of the trips wrong, while two of the supervised methods can classify all trips correctly. Using a large-scale dataset of 29037 trips, we find that separation between classes is less evident than in the labeled dataset and show that the choice of classification methods impacts the aggregated modal split estimate.

Dynamic Network Loading (DNL) models are typically formulated as a system of differential equations where travel times, densities or any other variable that indicates congestion is endogenous. However, such endogeneities increase the complexity of the Dynamic Traffic Assignment (DTA) problem due to the interdependence of DNL, route choice and demand. In this paper, attempting to exploit the growing accessibility of traffic-related data, we suggest that congestion can be instead captured by exogenous variables, such as travel time observations. We propagate the traffic flow based on an exogenous travel time function, which has a piece-wise linear form. Given piece-wise stationary route flows, the piece-wise linear form of the travel time function allows us to use an efficient event-based modelling structure. Our Data-Driven Network Loading (DDNL) approach is developed in accordance with the theoretical DNL framework ensuring vehicle conservation and FIFO. The first simulation experiment-based results are encouraging, indicating that the DDNL can contribute to improving the efficiency of applications where the monitoring of historical network-wide flows is required. Abbreviations: DDNL - Data Driven Network Loading; DNL - Dynamic Network Loading; DTA - Dynamic Traffic Assignment; ITS - Intelligent Transportation Systems; OD - Origin Destination; TTF - Travel Time Function; LTT - Linear Travel Time; DL - Demand level, Funding Agencies|Swedish Energy AgencySwedish Energy Agency [46963-1]

The introduction of automated vehicles (AVs) is commonly expected to improve different aspects of transportation. A long transition period is expected until AVs become prevalent on roads. During this period, different types of AVs with different driving logics will coexist along human driven vehicles. Using microscopic traffic simulation, this study investigates the range of potential impacts on traffic performance in terms of throughput and travel delays for different types of AVs and human driven vehicles on motorways. The simulation experiment includes scenarios with combinations of three different driving logics for AVs together with human driven vehicles at increasing penetration rates. The utilized AV driving logics represent the evolution of AVs, they were defined in the microscopic simulation tool Vissim and were created by modifying and extending the human driver behaviour models. The results of the simulation experiment show a decrease in vehicle throughput and significant effects on delay times when AVs with a more cautious driving logic are predominant. Overall, results show higher vehicle throughput and lower travel delays as AVs evolve to more advanced driving logics., Funding Agencies|Swedish Transport Administration (Trafikverket) [TRV 2016/20608, TRV 2019/27044]; European UnionEuropean Commission [H2020-ART-2016-2017, 723201]

Many applications in transport planning require an understanding of travel patterns separated by travel mode. To use cellular network data as observations of human mobility in these applications, classification by travel mode is needed. Existing classification methods for GPS-trajectories are often inefficient for cellular network data, which has lower resolution in space and time than GPS data. In this study, we compare three geometry-based mode classification methods and three supervised methods to classify trips extracted from cellular network data in intercity origin-destination pairs as either road or train. To understand the difficulty of the problem, we use a labeled dataset of 255 trips in two OD-pairs to train the supervised classification methods and to evaluate the classification performance. For an OD-pair where the road and train routes are not separated by more than four kilometers, the geometry-based methods classify 4.5% - 7.1% of the trips wrong, while two of the supervised methods can classify all trips correctly. Using a large-scale dataset of 29037 trips, we find that separation between classes is less evident than in the labeled dataset and show that the choice of classification methods impacts the aggregated modal split estimate.

Many applications in transport planning require an understanding of travel patterns separated by travel mode. To use cellular network data as observations of human mobility in these applications, classification by travel mode is needed. Existing classification methods for GPS-trajectories are often inefficient for cellular network data, which has lower resolution in space and time than GPS data. In this study, we compare three geometry-based mode classification methods and three supervised methods to classify trips extracted from cellular network data in intercity origin-destination pairs as either road or train. To understand the difficulty of the problem, we use a labeled dataset of 255 trips in two OD-pairs to train the supervised classification methods and to evaluate the classification performance. For an OD-pair where the road and train routes are not separated by more than four kilometers, the geometry-based methods classify 4.5% - 7.1% of the trips wrong, while two of the supervised methods can classify all trips correctly. Using a large-scale dataset of 29037 trips, we find that separation between classes is less evident than in the labeled dataset and show that the choice of classification methods impacts the aggregated modal split estimate.

Dynamic Network Loading (DNL) models are typically formulated as a system of differential equations where travel times, densities or any other variable that indicates congestion is endogenous. However, such endogeneities increase the complexity of the Dynamic Traffic Assignment (DTA) problem due to the interdependence of DNL, route choice and demand. In this paper, attempting to exploit the growing accessibility of traffic-related data, we suggest that congestion can be instead captured by exogenous variables, such as travel time observations. We propagate the traffic flow based on an exogenous travel time function, which has a piece-wise linear form. Given piece-wise stationary route flows, the piece-wise linear form of the travel time function allows us to use an efficient event-based modelling structure. Our Data-Driven Network Loading (DDNL) approach is developed in accordance with the theoretical DNL framework ensuring vehicle conservation and FIFO. The first simulation experiment-based results are encouraging, indicating that the DDNL can contribute to improving the efficiency of applications where the monitoring of historical network-wide flows is required. Abbreviations: DDNL - Data Driven Network Loading; DNL - Dynamic Network Loading; DTA - Dynamic Traffic Assignment; ITS - Intelligent Transportation Systems; OD - Origin Destination; TTF - Travel Time Function; LTT - Linear Travel Time; DL - Demand level, Funding Agencies|Swedish Energy AgencySwedish Energy Agency [46963-1]

Dynamic Network Loading (DNL) models are typically formulated as a system of differential equations where travel times, densities or any other variable that indicates congestion is endogenous. However, such endogeneities increase the complexity of the Dynamic Traffic Assignment (DTA) problem due to the interdependence of DNL, route choice and demand. In this paper, attempting to exploit the growing accessibility of traffic-related data, we suggest that congestion can be instead captured by exogenous variables, such as travel time observations. We propagate the traffic flow based on an exogenous travel time function, which has a piece-wise linear form. Given piece-wise stationary route flows, the piece-wise linear form of the travel time function allows us to use an efficient event-based modelling structure. Our Data-Driven Network Loading (DDNL) approach is developed in accordance with the theoretical DNL framework ensuring vehicle conservation and FIFO. The first simulation experiment-based results are encouraging, indicating that the DDNL can contribute to improving the efficiency of applications where the monitoring of historical network-wide flows is required. Abbreviations: DDNL - Data Driven Network Loading; DNL - Dynamic Network Loading; DTA - Dynamic Traffic Assignment; ITS - Intelligent Transportation Systems; OD - Origin Destination; TTF - Travel Time Function; LTT - Linear Travel Time; DL - Demand level, Funding Agencies|Swedish Energy AgencySwedish Energy Agency [46963-1]

Many applications in transport planning require an understanding of travel patterns separated by travel mode. To use cellular network data as observations of human mobility in these applications, classification by travel mode is needed. Existing classification methods for GPS-trajectories are often inefficient for cellular network data, which has lower resolution in space and time than GPS data. In this study, we compare three geometry-based mode classification methods and three supervised methods to classify trips extracted from cellular network data in intercity origin-destination pairs as either road or train. To understand the difficulty of the problem, we use a labeled dataset of 255 trips in two OD-pairs to train the supervised classification methods and to evaluate the classification performance. For an OD-pair where the road and train routes are not separated by more than four kilometers, the geometry-based methods classify 4.5% - 7.1% of the trips wrong, while two of the supervised methods can classify all trips correctly. Using a large-scale dataset of 29037 trips, we find that separation between classes is less evident than in the labeled dataset and show that the choice of classification methods impacts the aggregated modal split estimate.

Cost-benefit analysis (CBA) for cycling infrastructure investments are less sophistically developed compared to the ones for private cars and public transport, and one of main reasons is the lack of "well-developed" transport models for cycling. In this study, a dedicated transport model for cycling is used to appraise cycling infrastructure investments in Stockholm, Sweden. The model captures the impact of a change in cycling infrastructure on cycling route choice, mode choice, destination choice and trip generation and calculates cycling flow on link level. the generalised cost measure defined in the route choice model captures the impact of cycling infrastructure. Results suggest that although cycling flow on the links with investment may increase substantially, only a small share comes from modal shift and thus the external effects such as reducing car congestion and emissions are marginal. For all three scenarios investigated, over 97% of the benefits measured in the unit of generalised cost belong to the existing cyclists. The route choice model does not minimize travel time but generalised cost which also measures health, safety benefits and other possible benefits that may be considered by the cyclists when they choose to cycle. In fact, travel time saving benefits of the investments evaluated in this paper are all negative. The existing effect evaluation models therefore need to be adjusted to be more consistent with the transport model., Funding Agencies|Swedish Transport Administration [TRV 2015/107094]

Data on travel patterns and travel demand are an important input to today’s traffic models used for traffic planning. Traditionally, travel demand is modelled using census data, travel surveys, and traffic counts. Problems arise from the fact that the sample sizes are rather limited and that they are expensive to collect and update the data. Cellular network data are a promising large-scale data source to obtain a better understanding of human mobility. To infer travel demand, we propose a method that starts by extracting trips from cellular network data. To find out which types of trips can be extracted, we use a small-scale cellular network dataset collected from 20 mobile phones together with GPS tracks collected on the same device. Using a large-scale dataset of cellular network data from a Swedish operator for the municipality of Norrköping, we compare the travel demand inferred from cellular network data to the municipality’s existing urban travel demand model as well as public transit tap-ins. The results for the small-scale dataset show that, with the proposed trip extraction methods, the recall (trip detection rate) is about 50% for short trips of 1-2 km, while it is 75–80% for trips of more than 5 km. Similarly, the recall also differs by a travel mode with more than 80% for public transit, 74% for car, but only 53% for bicycle and walking. After aggregating trips into an origin-destination matrix, the correlation is weak () using the original zoning used in the travel demand model with 189 zones, while it is significant with when aggregating to 24 zones. We find that the choice of the trip extraction method is crucial for the travel demand estimation as we find that the choice of the trip extraction method is crucial for the travel demandestimation as we find systematic differences in the resulting travel demand matrices using two different methods., Funding agency: The Swedish Agency for Innovation Systems (Vinnova) (grant number 2013-03077).

Data on travel patterns and travel demand are an important input to today’s traffic models used for traffic planning. Traditionally, travel demand is modelled using census data, travel surveys, and traffic counts. Problems arise from the fact that the sample sizes are rather limited and that they are expensive to collect and update the data. Cellular network data are a promising large-scale data source to obtain a better understanding of human mobility. To infer travel demand, we propose a method that starts by extracting trips from cellular network data. To find out which types of trips can be extracted, we use a small-scale cellular network dataset collected from 20 mobile phones together with GPS tracks collected on the same device. Using a large-scale dataset of cellular network data from a Swedish operator for the municipality of Norrköping, we compare the travel demand inferred from cellular network data to the municipality’s existing urban travel demand model as well as public transit tap-ins. The results for the small-scale dataset show that, with the proposed trip extraction methods, the recall (trip detection rate) is about 50% for short trips of 1-2 km, while it is 75–80% for trips of more than 5 km. Similarly, the recall also differs by a travel mode with more than 80% for public transit, 74% for car, but only 53% for bicycle and walking. After aggregating trips into an origin-destination matrix, the correlation is weak () using the original zoning used in the travel demand model with 189 zones, while it is significant with when aggregating to 24 zones. We find that the choice of the trip extraction method is crucial for the travel demand estimation as we find that the choice of the trip extraction method is crucial for the travel demandestimation as we find systematic differences in the resulting travel demand matrices using two different methods., Funding agency: The Swedish Agency for Innovation Systems (Vinnova) (grant number 2013-03077).

Data on travel patterns and travel demand are an important input to today’s traffic models used for traffic planning. Traditionally, travel demand is modelled using census data, travel surveys, and traffic counts. Problems arise from the fact that the sample sizes are rather limited and that they are expensive to collect and update the data. Cellular network data are a promising large-scale data source to obtain a better understanding of human mobility. To infer travel demand, we propose a method that starts by extracting trips from cellular network data. To find out which types of trips can be extracted, we use a small-scale cellular network dataset collected from 20 mobile phones together with GPS tracks collected on the same device. Using a large-scale dataset of cellular network data from a Swedish operator for the municipality of Norrköping, we compare the travel demand inferred from cellular network data to the municipality’s existing urban travel demand model as well as public transit tap-ins. The results for the small-scale dataset show that, with the proposed trip extraction methods, the recall (trip detection rate) is about 50% for short trips of 1-2 km, while it is 75–80% for trips of more than 5 km. Similarly, the recall also differs by a travel mode with more than 80% for public transit, 74% for car, but only 53% for bicycle and walking. After aggregating trips into an origin-destination matrix, the correlation is weak () using the original zoning used in the travel demand model with 189 zones, while it is significant with when aggregating to 24 zones. We find that the choice of the trip extraction method is crucial for the travel demand estimation as we find that the choice of the trip extraction method is crucial for the travel demandestimation as we find systematic differences in the resulting travel demand matrices using two different methods., Funding agency: The Swedish Agency for Innovation Systems (Vinnova) (grant number 2013-03077).

The aim of the work presented here is to develop a mathematical model that can assist decision-makers in selecting the optimal network of subsidised routes in air transportation. Sweden is used as a case study. The results show that even if most of the Swedish population already has a good accessibility, the model can suggest new routes that can further improve accessibility without increasing the subsidisation cost. The closure of an airport reduces the available commercial routes, which would impair the accessibility to a given destination; a subsidised route would hence be required as a replacement. The ability of the model to consider several accessibility criteria makes it useful for managers at transportation authorities when making airport location decisions., Funding Agencies|Swedish Transport Administration (Trafikverket)

The aim of the work presented here is to develop a mathematical model that can assist decision-makers in selecting the optimal network of subsidised routes in air transportation. Sweden is used as a case study. The results show that even if most of the Swedish population already has a good accessibility, the model can suggest new routes that can further improve accessibility without increasing the subsidisation cost. The closure of an airport reduces the available commercial routes, which would impair the accessibility to a given destination; a subsidised route would hence be required as a replacement. The ability of the model to consider several accessibility criteria makes it useful for managers at transportation authorities when making airport location decisions., Funding Agencies|Swedish Transport Administration (Trafikverket)

The aim of the work presented here is to develop a mathematical model that can assist decision-makers in selecting the optimal network of subsidised routes in air transportation. Sweden is used as a case study. The results show that even if most of the Swedish population already has a good accessibility, the model can suggest new routes that can further improve accessibility without increasing the subsidisation cost. The closure of an airport reduces the available commercial routes, which would impair the accessibility to a given destination; a subsidised route would hence be required as a replacement. The ability of the model to consider several accessibility criteria makes it useful for managers at transportation authorities when making airport location decisions., Funding Agencies|Swedish Transport Administration (Trafikverket)

Data on travel patterns and travel demand are an important input to today’s traffic models used for traffic planning. Traditionally, travel demand is modelled using census data, travel surveys, and traffic counts. Problems arise from the fact that the sample sizes are rather limited and that they are expensive to collect and update the data. Cellular network data are a promising large-scale data source to obtain a better understanding of human mobility. To infer travel demand, we propose a method that starts by extracting trips from cellular network data. To find out which types of trips can be extracted, we use a small-scale cellular network dataset collected from 20 mobile phones together with GPS tracks collected on the same device. Using a large-scale dataset of cellular network data from a Swedish operator for the municipality of Norrköping, we compare the travel demand inferred from cellular network data to the municipality’s existing urban travel demand model as well as public transit tap-ins. The results for the small-scale dataset show that, with the proposed trip extraction methods, the recall (trip detection rate) is about 50% for short trips of 1-2 km, while it is 75–80% for trips of more than 5 km. Similarly, the recall also differs by a travel mode with more than 80% for public transit, 74% for car, but only 53% for bicycle and walking. After aggregating trips into an origin-destination matrix, the correlation is weak () using the original zoning used in the travel demand model with 189 zones, while it is significant with when aggregating to 24 zones. We find that the choice of the trip extraction method is crucial for the travel demand estimation as we find that the choice of the trip extraction method is crucial for the travel demandestimation as we find systematic differences in the resulting travel demand matrices using two different methods., Funding agency: The Swedish Agency for Innovation Systems (Vinnova) (grant number 2013-03077).

Data on travel patterns and travel demand are an important input to today’s traffic models used for traffic planning. Traditionally, travel demand is modelled using census data, travel surveys, and traffic counts. Problems arise from the fact that the sample sizes are rather limited and that they are expensive to collect and update the data. Cellular network data are a promising large-scale data source to obtain a better understanding of human mobility. To infer travel demand, we propose a method that starts by extracting trips from cellular network data. To find out which types of trips can be extracted, we use a small-scale cellular network dataset collected from 20 mobile phones together with GPS tracks collected on the same device. Using a large-scale dataset of cellular network data from a Swedish operator for the municipality of Norrköping, we compare the travel demand inferred from cellular network data to the municipality’s existing urban travel demand model as well as public transit tap-ins. The results for the small-scale dataset show that, with the proposed trip extraction methods, the recall (trip detection rate) is about 50% for short trips of 1-2 km, while it is 75–80% for trips of more than 5 km. Similarly, the recall also differs by a travel mode with more than 80% for public transit, 74% for car, but only 53% for bicycle and walking. After aggregating trips into an origin-destination matrix, the correlation is weak () using the original zoning used in the travel demand model with 189 zones, while it is significant with when aggregating to 24 zones. We find that the choice of the trip extraction method is crucial for the travel demand estimation as we find that the choice of the trip extraction method is crucial for the travel demandestimation as we find systematic differences in the resulting travel demand matrices using two different methods., Funding agency: The Swedish Agency for Innovation Systems (Vinnova) (grant number 2013-03077).

The aim of the work presented here is to develop a mathematical model that can assist decision-makers in selecting the optimal network of subsidised routes in air transportation. Sweden is used as a case study. The results show that even if most of the Swedish population already has a good accessibility, the model can suggest new routes that can further improve accessibility without increasing the subsidisation cost. The closure of an airport reduces the available commercial routes, which would impair the accessibility to a given destination; a subsidised route would hence be required as a replacement. The ability of the model to consider several accessibility criteria makes it useful for managers at transportation authorities when making airport location decisions., Funding Agencies|Swedish Transport Administration (Trafikverket)

The aim of the work presented here is to develop a mathematical model that can assist decision-makers in selecting the optimal network of subsidised routes in air transportation. Sweden is used as a case study. The results show that even if most of the Swedish population already has a good accessibility, the model can suggest new routes that can further improve accessibility without increasing the subsidisation cost. The closure of an airport reduces the available commercial routes, which would impair the accessibility to a given destination; a subsidised route would hence be required as a replacement. The ability of the model to consider several accessibility criteria makes it useful for managers at transportation authorities when making airport location decisions., Funding Agencies|Swedish Transport Administration (Trafikverket)

Data on travel patterns and travel demand are an important input to today’s traffic models used for traffic planning. Traditionally, travel demand is modelled using census data, travel surveys, and traffic counts. Problems arise from the fact that the sample sizes are rather limited and that they are expensive to collect and update the data. Cellular network data are a promising large-scale data source to obtain a better understanding of human mobility. To infer travel demand, we propose a method that starts by extracting trips from cellular network data. To find out which types of trips can be extracted, we use a small-scale cellular network dataset collected from 20 mobile phones together with GPS tracks collected on the same device. Using a large-scale dataset of cellular network data from a Swedish operator for the municipality of Norrköping, we compare the travel demand inferred from cellular network data to the municipality’s existing urban travel demand model as well as public transit tap-ins. The results for the small-scale dataset show that, with the proposed trip extraction methods, the recall (trip detection rate) is about 50% for short trips of 1-2 km, while it is 75–80% for trips of more than 5 km. Similarly, the recall also differs by a travel mode with more than 80% for public transit, 74% for car, but only 53% for bicycle and walking. After aggregating trips into an origin-destination matrix, the correlation is weak () using the original zoning used in the travel demand model with 189 zones, while it is significant with when aggregating to 24 zones. We find that the choice of the trip extraction method is crucial for the travel demand estimation as we find that the choice of the trip extraction method is crucial for the travel demandestimation as we find systematic differences in the resulting travel demand matrices using two different methods., Funding agency: The Swedish Agency for Innovation Systems (Vinnova) (grant number 2013-03077).

This study presents a transport model to better model cycling demand. The model improves modelling of cycling in several ways compared to a conventional transport model. First, it uses a detailed bicycle network containing information about existing bicycle infrastructure. Second, generalised cost measures based on different bicycle route choice models are calculated and compared to evaluate how to best represent the impact of bicycle infrastructure in the model. Third, the model utilizes a refined zone system with smaller zones of size 250 m x 250 m. Using these smaller zones, more short-distance tours are included in the model, and these are predominantly walking and cycling trips. Fourth, the model considers cycling also as an access mode choice to public transport. Therefore, the model treats cycling and public transport as both competing and complementary modes. Results show that the model captures detailed individual heterogeneity in cycling demand for different trip purposes. Impacts of bicycle infrastructure, land use characteristics and individual/household socio-demographics are investigated. Detailed individual level travel time and generalised cost are derived for cyclists of different socio-demographics. The result highlights the importance of choosing a good measure of generalised cost, given that different bicycle route choice models result in different effects of bicycle infrastructure. In future applications, the model can be used to evaluate proposed bicycle investments regarding their impact on link flow, bicycle route choice, modal shift and generation of completely new tours. The model can also be a powerful tool in a cost-benefit analysis of bicycle investments., Funding Agencies|Swedish Transport Administration [TRV 2015/107094]

Heterogeneous data collected by smartphone sensors offer new opportunities to study a person’s mobility behavior. The mobility patterns extracted from the travel histories found in these data enable agents residing in mobile devices to model transitions between visited locations, so that upcoming trips can be predicted after observing a set of events, and assistance can be planned in advance. When several agents cooperate, the forecasted trips made by multiple users can provide a potential supply for shared mobility systems such as dynamic ridesharing. These trips must be sufficiently regular and frequent to be reliably announced as shareable trips. This paper describes a methodology to identify a predictable aggregate supply for ridesharing via mobility patterns discovered in users’ travel histories. The methodology empirically quantifies measures like the regularity and frequency of these patterns on a dataset consisting of 967 users scattered across different geographical areas. The sample exhibits high heterogeneity with respect to these measures (hence, of predictability, regardless of the prediction method). This paper shows how frequency of trip patterns decreases, while regularity increases, when additional dimensions such as departure times are added to the analysis. It was concluded that the traveler flexibility with regard to accepting less regular trips is vital to discover a larger supply. These results provide insights to develop future applications able to take advantage of this approach, to increase ridesharing rates, allowing a critical mass to be more easily attained., Funding agencies: National Secretariat of Higher Education, Science, Technology and Innovation of Ecuador (SENESCYT)

Heterogeneous data collected by smartphone sensors offer new opportunities to study a person’s mobility behavior. The mobility patterns extracted from the travel histories found in these data enable agents residing in mobile devices to model transitions between visited locations, so that upcoming trips can be predicted after observing a set of events, and assistance can be planned in advance. When several agents cooperate, the forecasted trips made by multiple users can provide a potential supply for shared mobility systems such as dynamic ridesharing. These trips must be sufficiently regular and frequent to be reliably announced as shareable trips. This paper describes a methodology to identify a predictable aggregate supply for ridesharing via mobility patterns discovered in users’ travel histories. The methodology empirically quantifies measures like the regularity and frequency of these patterns on a dataset consisting of 967 users scattered across different geographical areas. The sample exhibits high heterogeneity with respect to these measures (hence, of predictability, regardless of the prediction method). This paper shows how frequency of trip patterns decreases, while regularity increases, when additional dimensions such as departure times are added to the analysis. It was concluded that the traveler flexibility with regard to accepting less regular trips is vital to discover a larger supply. These results provide insights to develop future applications able to take advantage of this approach, to increase ridesharing rates, allowing a critical mass to be more easily attained., Funding agencies: National Secretariat of Higher Education, Science, Technology and Innovation of Ecuador (SENESCYT)

Smart cities and communities rely on efficient, reliable and robust transport systems. Managing urban public transport systems is becoming increasingly challenging with a pronounced shift towards multiple actors operating in a multi-modal multi-level networks. This calls for the development of an integrated passenger-focused management approach which takes advantage of multiple data sources and state-of-the-art scheduling support. The TRANS-FORM project is developing, implementing and testing a data driven decision making tool that will support smart planning and proactive and adaptive operations. The tool will integrate new concepts and methods of behavioral modelling, passenger flow forecasting and network state predictions into real-time operations. In this study we present the first step in this direction which consists of an empirical analysis of passenger flows to infer travel patterns and service reliability properties. Data mining and transport flow analysis are used to investigate network dynamics at different scales.

Smart cities and communities rely on efficient, reliable and robust transport systems. Managing urban public transport systems is becoming increasingly challenging with a pronounced shift towards multiple actors operating in a multi-modal multi-level networks. This calls for the development of an integrated passenger-focused management approach which takes advantage of multiple data sources and state-of-the-art scheduling support. The TRANS-FORM project is developing, implementing and testing a data driven decision making tool that will support smart planning and proactive and adaptive operations. The tool will integrate new concepts and methods of behavioral modelling, passenger flow forecasting and network state predictions into real-time operations. In this study we present the first step in this direction which consists of an empirical analysis of passenger flows to infer travel patterns and service reliability properties. Data mining and transport flow analysis are used to investigate network dynamics at different scales.

Smart cities and communities rely on efficient, reliable and robust transport systems. Managing urban public transport systems is becoming increasingly challenging with a pronounced shift towards multiple actors operating in a multi-modal multi-level networks. This calls for the development of an integrated passenger-focused management approach which takes advantage of multiple data sources and state-of-the-art scheduling support. The TRANS-FORM project is developing, implementing and testing a data driven decision making tool that will support smart planning and proactive and adaptive operations. The tool will integrate new concepts and methods of behavioral modelling, passenger flow forecasting and network state predictions into real-time operations. In this study we present the first step in this direction which consists of an empirical analysis of passenger flows to infer travel patterns and service reliability properties. Data mining and transport flow analysis are used to investigate network dynamics at different scales.

Heterogeneous data collected by smartphone sensors offer new opportunities to study a person’s mobility behavior. The mobility patterns extracted from the travel histories found in these data enable agents residing in mobile devices to model transitions between visited locations, so that upcoming trips can be predicted after observing a set of events, and assistance can be planned in advance. When several agents cooperate, the forecasted trips made by multiple users can provide a potential supply for shared mobility systems such as dynamic ridesharing. These trips must be sufficiently regular and frequent to be reliably announced as shareable trips. This paper describes a methodology to identify a predictable aggregate supply for ridesharing via mobility patterns discovered in users’ travel histories. The methodology empirically quantifies measures like the regularity and frequency of these patterns on a dataset consisting of 967 users scattered across different geographical areas. The sample exhibits high heterogeneity with respect to these measures (hence, of predictability, regardless of the prediction method). This paper shows how frequency of trip patterns decreases, while regularity increases, when additional dimensions such as departure times are added to the analysis. It was concluded that the traveler flexibility with regard to accepting less regular trips is vital to discover a larger supply. These results provide insights to develop future applications able to take advantage of this approach, to increase ridesharing rates, allowing a critical mass to be more easily attained., Funding agencies: National Secretariat of Higher Education, Science, Technology and Innovation of Ecuador (SENESCYT)

Smart cities and communities rely on efficient, reliable and robust transport systems. Managing urban public transport systems is becoming increasingly challenging with a pronounced shift towards multiple actors operating in a multi-modal multi-level networks. This calls for the development of an integrated passenger-focused management approach which takes advantage of multiple data sources and state-of-the-art scheduling support. The TRANS-FORM project is developing, implementing and testing a data driven decision making tool that will support smart planning and proactive and adaptive operations. The tool will integrate new concepts and methods of behavioral modelling, passenger flow forecasting and network state predictions into real-time operations. In this study we present the first step in this direction which consists of an empirical analysis of passenger flows to infer travel patterns and service reliability properties. Data mining and transport flow analysis are used to investigate network dynamics at different scales.

Heterogeneous data collected by smartphone sensors offer new opportunities to study a person’s mobility behavior. The mobility patterns extracted from the travel histories found in these data enable agents residing in mobile devices to model transitions between visited locations, so that upcoming trips can be predicted after observing a set of events, and assistance can be planned in advance. When several agents cooperate, the forecasted trips made by multiple users can provide a potential supply for shared mobility systems such as dynamic ridesharing. These trips must be sufficiently regular and frequent to be reliably announced as shareable trips. This paper describes a methodology to identify a predictable aggregate supply for ridesharing via mobility patterns discovered in users’ travel histories. The methodology empirically quantifies measures like the regularity and frequency of these patterns on a dataset consisting of 967 users scattered across different geographical areas. The sample exhibits high heterogeneity with respect to these measures (hence, of predictability, regardless of the prediction method). This paper shows how frequency of trip patterns decreases, while regularity increases, when additional dimensions such as departure times are added to the analysis. It was concluded that the traveler flexibility with regard to accepting less regular trips is vital to discover a larger supply. These results provide insights to develop future applications able to take advantage of this approach, to increase ridesharing rates, allowing a critical mass to be more easily attained., Funding agencies: National Secretariat of Higher Education, Science, Technology and Innovation of Ecuador (SENESCYT)