Analysis and operational challenges of dynamic ride sharing demand responsive transportation models

There is a wide body of evidence that suggests sustainable mobility is not only a technological question, but that automotive technology will be a part of the solution in becoming a necessary albeit insufficient condition. Sufficiency is emerging as a paradigm shift from car ownership to vehicle usage, which is a consequence of socio-economic changes. Information and Communication Technologies (ICT) now make it possible for a user to access a mobility service to go anywhere at any time. Among the many emerging mobility services, Multiple Passenger Ridesharing and its variants look the most promising. However, challenges arise in implementing these systems while accounting specifically for time dependencies and time windows that reflect users’ needs, specifically in terms of real-time fleet dispatching and dynamic route calculation. On the other hand, we must consider the feasibility and impact analysis of the many factors influencing the behavior of the system – as, for example, service demand, the size of the service fleet, the capacity of the shared vehicles and whether the time window requirements are soft or tight. This paper analyzes - a Decision Support System that computes solutions with ad hoc heuristics applied to variants of Pick Up and Delivery Problems with Time Windows, as well as to Feasibility and Profitability criteria rooted in Dynamic Insertion Heuristics. To evaluate the applications, a Simulation Framework is proposed. It is based on a microscopic simulation model that emulates real-time traffic conditions and a real traffic information system. It also interacts with the Decision Support System by feeding it with the required data for making decisions in the simulation that emulate the behavior of the shared fleet. The proposed simulation framework has been implemented in a model of Barcelona’s Central Business District. The obtained results prove the potential feasibility of the mobility concept.Postprint (published version

A Kalman-filter approach for dynamic OD estimation in corridors based on bluetooth and Wi-Fi data collection

From the point of view of the information supplied by an ATIS to the motorists entering a freeway of one of the most relevant is the Forecasted Travel Time, that is the expected travel time that they will experience when traverse a freeway segment. From the point of view of ATMS, the dynamic estimates of time dependencies in OD matrices is a major input to dynamic traffic models used for estimating the current traffic state and forecasting its short term evolution. Travel Time Forecasting and Dynamic OD Estimation are thus two key components of ATIS/ATMS and the quality of the results that they could provide depend not only on the quality of the models but also on the accuracy and reliability of the measurements of traffic variables supplied by the detection technology. The quality and reliability of the measurements produced by traditional technologies, as inductive loop detectors, is not usually the required by real-time applications, therefore one wonders what could be expected from the new ICT technologies, as for example Automatic Vehicle Location, License Plate Recognition, detection of mobile devices and so on. A simulation experiment is proposed prior to deploy the technology for a pilot project. The simulation emulates the logging and time stamping of a sample of equipped vehicles providing real-time estimates of travel times for the whole population of vehicles and OD pattern of the equipped vehicles are considered real-time estimates of the dynamic OD pattern for the whole population of vehicles. The main objective of this paper is to explore the quality of the data produced by the Bluetooth and Wi-Fi detection of mobile devices equipping vehicles to estimate time dependent OD matrices. Ad hoc procedures based on Kalman Filtering have been designed and implemented successfully and the numerical results of the computational experiments are presented and discussed.Postprint (published version

Exploring the use of traffic data collected from new ICT based sensors to estimate time dependent OD matrices

Peer ReviewedPostprint (author’s final draft

ICT based estimation of time-dependent origin-destination matrices

Time-Dependent Origin-Destination (OD) matrices are a key input to Dynamic Traffic Models, microscopic and mesoscopic traffic simulators are relevant examples of such models, traditionally used to assist in the design and evaluation of Traffic Management and Information Systems (ATMS/ATIS). Dynamic traffic models are also starting to be used to support real-time traffic management decisions. The typical approaches to the time-dependent OD estimation have been based either on ad hoc heuristics using mathematical programming approaches, or on Kalman-Filtering. The advent of the new Information and Communication Technologies (ICT), as for example Automatic Vehicle Location, License Plate Recognition, detection of mobile devices, Vehicle to Infrastructure (V2I) and so on, makes available new types of traffic data of higher quality and accuracy allowing for new modeling hypothesis leading to more computationally efficient algorithms. This paper extends the previous research on Kalman Filtering approaches for Freeway OD estimation using these data, to more complex topologies of urban networks were alternative path choices between origins and destinations are available. Ad hoc procedures based on Kalman Filtering have been designed and implemented successfully and the numerical results of the computational experiments are presented and discussed.Postprint (author’s final draft

A Kalman-filter approach for dynamic OD estimation in corridors based on bluetooth and wifi data collection

From the point of view of the information supplied by an ATIS to the motorists entering a freeway of one of the most relevant is the Forecasted Travel Time, that is the expected travel time that they will experience when traverse a freeway segment. From the point of view of ATMS, the dynamic estimates of time dependencies in OD matrices is a major input to dynamic traffic models used for estimating the current traffic state and forecasting its short term evolution. Travel Time Forecasting and Dynamic OD Estimation are thus two key components of ATIS/ATMS and the quality of the results that they could provide depend not only on the quality of the models but also on the accuracy and reliability of the measurements of traffic variables supplied by the detection technology. The quality and reliability of the measurements produced by traditional technologies, as inductive loop detectors, is not usually the required by real-time applications, therefore one wonders what could be expected from the new ICT technologies, as for example Automatic Vehicle Location, License Plate Recognition, detection of mobile devices and so on. A simulation experiment is proposed prior to deploy the technology for a pilot project. The simulation emulates the logging and time stamping of a sample of equipped vehicles providing real-time estimates of travel times for the whole population of vehicles and OD pattern of the equipped vehicles are considered real-time estimates of the dynamic OD pattern for the whole population of vehicles. The main objective of this paper is to explore the quality of the data produced by the Bluetooth and Wi-Fi detection of mobile devices equipping vehicles to estimate time dependent OD matrices. Ad hoc procedures based on Kalman Filtering have been designed and implemented successfully and the numerical results of the computational experiments are presented and discussed.Peer ReviewedPostprint (author’s final draft

Travel time forecasting and dynamic of estimation in freeways based on bluetooth traffic monitoring

Peer ReviewedPostprint (author’s final draft

Dynamic OD matrix estimation exploiting bluetooth data in urban networkss

Time-Dependent Origin-Destination (OD) matrices are a key input to Dynamic Traffic Models. Microscopic and Mesoscopic traffic simulators are relevant examples of such models, traditionally used to assist in the design and evaluation of Traffic Management and Information Systems (ATMS/ATIS). Dynamic traffic models can also be used to support real-time traffic management decisions. The typical approaches to time-dependent OD estimation have been based either on Kalman-Filtering or on bi-level mathematical programming approaches that can be considered in most cases as ad hoc heuristics. The advent of the new Information and Communication Technologies (ICT) makes available new types of traffic data with higher quality and accuracy, allowing new modeling hypotheses which lead to more computationally efficient algorithms. This paper presents a Kalman Filtering approach, that explicitly exploit traffic data available from Bluetooth sensors, and reports computational experiments for networks and corridors.Peer ReviewedPostprint (published version

A Kalman filter approach for the estimation of time dependent OD matrices exploiting bluetooth traffic data collection

Time-Dependent Origin-Destination (OD) matrices are a key input to Dynamic Traffic Models, microscopic and mesoscopic traffic simulators are relevant examples of such models, traditionally used to assist in the design and evaluation of Traffic Management and Information Systems (ATMS/ATIS). Dynamic traffic models can also be used to support real-time traffic management decisions. The typical approaches to the time-dependent OD estimation have been based either on ad hoc heuristics using mathematical programming approaches, or on Kalman-Filtering. The advent of the new Information and Communication Technologies (ICT), makes available new types of traffic data of higher quality and accuracy allowing for new modeling hypothesis leading to more computationally efficient algorithms. Ad hoc procedures based on Kalman Filtering, explicitly exploiting traffic data available from Bluetooth sensors, have been designed and implemented successfully and the numerical results of the computational experiments are discussed for freeway and network test sites.Peer ReviewedPostprint (published version

A Kalman filter approach for exploiting bluetooth traffic data when estimating time-dependent OD matrices

Time-dependent origin–destination (OD) matrices are essential input for dynamic traffic models such as microscopic and mesoscopic traffic simulators. Dynamic traffic models also support real-time traffic management decisions, and they are traditionally used in the design and evaluation of advanced traffic traffic management and information systems (ATMS/ATIS). Time-dependent OD estimations are typically based either on Kalman filtering or on bilevel mathematical programming, which can be considered in most cases as ad hoc heuristics. The advent of the new information and communication technologies (ICT) provides new types of traffic data with higher quality and accuracy, which in turn allows new modeling hypotheses that lead to more computationally efficient algorithms. This article presents ad hoc, Kalman filtering procedures that explicitly exploit Bluetooth sensor traffic data, and it reports the numerical results from computational experiments performed at a network test site.Peer ReviewedPostprint (author’s final draft