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

Engaging Undergraduate Students in Transportation Studies through Simulating Transportation for Realistic Engineering Education and Training (STREET)

The practice of transportation engineering and planning has evolved substantially over the past several decades. A new paradigm for transportation engineering education is required to better engage students and deliver knowledge. Simulation tools have been used by transportation professionals to evaluate and analyze the potential impact of design or control strategy changes. Conveying complex transportation concepts can be effectively achieved by exploring them through simulation. Simulation is particularly valuable in transportation education because most transportation policies and strategies in the real world take years to implement with a prohibitively high cost. Transportation simulation allows learners to apply different control strategies in a risk-free environment and to expose themselves to transportation engineering methodologies that are currently in practice. Despite the advantages, simulation, however, has not been widely adopted in the education of transportation engineering. Using simulation in undergraduate transportation courses is sporadic and reported efforts have been focused on the upper-level technical elective courses. A suite of web-based simulation modules was developed and incorporated in the undergraduate transportation courses at University of Minnesota. The STREET (Simulating Transportation for Realistic Engineering Education and Training) research project was recently awarded by NSF (National Science Foundation) to develop web-based simulation modules to improve instruction in transportation engineering courses and evaluate their effectiveness. Our ultimate goal is to become the epicenter for developing simulation-based teaching materials, an active textbook, which offers an interactive learning environment to undergraduate students. With the hand-on nature of simulation, we hope to improve student understanding of critical concepts in transportation engineering and student motivation toward transportation engineering, and improve student retention in the field. We also would like to disseminate the results and teaching materials to other colleges to integrate the simulation modules in their curricula.Transportation Education and Training, Transportation Simulation, Roadway Geometry Design

Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction

Tracking congestion throughout the network road is a critical component of Intelligent transportation network management systems. Understanding how the traffic flows and short-term prediction of congestion occurrence due to rush-hour or incidents can be beneficial to such systems to effectively manage and direct the traffic to the most appropriate detours. Many of the current traffic flow prediction systems are designed by utilizing a central processing component where the prediction is carried out through aggregation of the information gathered from all measuring stations. However, centralized systems are not scalable and fail provide real-time feedback to the system whereas in a decentralized scheme, each node is responsible to predict its own short-term congestion based on the local current measurements in neighboring nodes. We propose a decentralized deep learning-based method where each node accurately predicts its own congestion state in real-time based on the congestion state of the neighboring stations. Moreover, historical data from the deployment site is not required, which makes the proposed method more suitable for newly installed stations. In order to achieve higher performance, we introduce a regularized Euclidean loss function that favors high congestion samples over low congestion samples to avoid the impact of the unbalanced training dataset. A novel dataset for this purpose is designed based on the traffic data obtained from traffic control stations in northern California. Extensive experiments conducted on the designed benchmark reflect a successful congestion prediction