Ride Sharing with a Vehicle of Unlimited Capacity

A ride sharing problem is considered where we are given a graph, whose edges are equipped with a travel cost, plus a set of objects, each associated with a transportation request given by a pair of origin and destination nodes. A vehicle travels through the graph, carrying each object from its origin to its destination without any bound on the number of objects that can be simultaneously transported. The vehicle starts and terminates its ride at given nodes, and the goal is to compute a minimum-cost ride satisfying all requests. This ride sharing problem is shown to be tractable on paths by designing a O(h*log(h)+n) algorithm, with h being the number of distinct requests and with n being the number of nodes in the path. The algorithm is then used as a subroutine to efficiently solve instances defined over cycles, hence covering all graphs with maximum degree 2. This traces the frontier of tractability, since NP-hard instances are exhibited over trees whose maximum degree is 3

A Branch-and-Price Algorithm for the Electric Autonomous Dial-A-Ride Problem

The Electric Autonomous Dial-A-Ride Problem (E-ADARP) consists in scheduling a fleet of electric autonomous vehicles to provide ride-sharing services for customers that specify their origins and destinations. The E-ADARP differs from the classical DARP in two aspects: (i) a weighted-sum objective that minimizes both total travel time and total excess user ride time; (ii) the employment of electric autonomous vehicles and a partial recharging policy. This paper presents a highly-efficient labeling algorithm, which is integrated into Branch-and-Price (B&P) algorithms to solve the E-ADARP. To handle (i), we introduce a fragment-based representation of paths. A novel approach is invoked to abstract fragments to arcs while ensuring excess-user-ride-time optimality. We then construct a new graph that preserves all feasible routes of the original graph by enumerating all feasible fragments, abstracting them to arcs, and connecting them with each other, depots, and recharging stations in a feasible way. On the new graph, partial recharging (ii) is tackled exactly by tailored Resource Extension Functions (REFs). We apply strong dominance rules and constant-time feasibility checks to compute the shortest paths efficiently. These methods construct the first labeling algorithm that can deal with minimizing (excess) user ride time. In the computational experiments, the B&P algorithm achieves optimality in 71 out of 84 instances. Remarkably, among these instances, 50 were solved optimally at the root node without branching. We identify 26 new best solutions, improve 30 previously reported lower bounds, and provide 17 new lower bounds for large-scale instances with up to 8 vehicles and 96 requests. In total 42 new best solutions are generated on previously solved and unsolved instances

United We Ride National Dialogue

The Coordinating Council on Access and Mobility (CCAM) asked the National Academy of Public Administration and Easter Seals Project ACTION to develop and host the first United We Ride (UWR) National Dialogue. The goal of the Dialogue was to help shape future policy direction and provide input to the next CCAM strategic plan. The National Academy also assembled a small work group with representatives of the Federal Interagency Coordinating Council on Access and Mobility, Easter Seals Project ACTION, and the National Resource Center on Human Service Transportation to help guide the process of design and implementation.The CCAM includes 11 federal departments, nine of which are responsible for providing transportation for people with disabilities, older adults, and people with limited incomes. CCAM officially launched United We Ride in 2004 to (1) provide more rides for target populations while using the same or fewer assets, (2) simplify access, and (3) increase customer satisfaction.Key FindingsThe process used to create coordinated transportation plans needs improvement. Significant federal policy barriers still exist to strategies that would facilitate access to transportation services. Mobility management strategies are underutilized in communities across the country, and missed opportunities to bridge gaps between transportation and other community services still need to be addressed

A Decomposition Algorithm to Solve the Multi-Hop Peer-to-Peer Ride-Matching Problem

In this paper, we mathematically model the multi-hop Peer-to-Peer (P2P) ride-matching problem as a binary program. We formulate this problem as a many-to-many problem in which a rider can travel by transferring between multiple drivers, and a driver can carry multiple riders. We propose a pre-processing procedure to reduce the size of the problem, and devise a decomposition algorithm to solve the original ride-matching problem to optimality by means of solving multiple smaller problems. We conduct extensive numerical experiments to demonstrate the computational efficiency of the proposed algorithm and show its practical applicability to reasonably-sized dynamic ride-matching contexts. Finally, in the interest of even lower solution times, we propose heuristic solution methods, and investigate the trade-offs between solution time and accuracy

Long-term mobility choice considering availability effects of shared and new mobility services

E-bikes, shared and new mobility services such as Mobility-as-a-Service (MaaS) are emerging as sustainable and healthy alternatives to private cars, introducing complexities in household mobility decisions and potential substitution between transportation modes and services. However, existing studies primarily examined the potential long-term adoption of these emerging mobilities separately, leaving a gap in understanding the interplay among various emerging mobilities and conventional cars. This study therefore addresses this portfolio choice incorporating a stated portfolio choice experiment encompassing pedelecs, speed pedelecs, MaaS, Shared e-Mobilities, and electric and conventional cars. Results from a random effects error component mixed logit model, based on an online survey conducted in the Netherlands, indicate significant availability effects of shared and new mobility services on personal mobility ownership decisions, and a substantial demand for pedelecs. The findings contribute to facilitating the adoption of emerging mobilities with enhanced synergy, as shared and new mobility services are gradually becoming available

Instruments of Transport Policy.

The material in this Working Paper was generated as input to DETR's Guidance on the Methodology for Multi Modal Studies (GOMMMS). DETR subsequently decided only to provide summary information on transport policy measures, and to leave the consultants involved in individual multi modal studies to make their own assessment of individual policy measures in the context of specific study areas. It has been decided to make this fuller document available as a reference source. The purpose of the review of policy measures was to provide summary information on the range of policy measures available, experience of their use and, based on past studies, their potential contribution to the range of policy objectives specified for GOMMMS. The review was based on an earlier one included in the Institution of Highways and Transportation's Guidelines on Developing Urban Transport Strategies (1996). This material was updated using references published since 1996 and expanded to cover policy measures relevant in inter-urban areas. It had been intended to circulate it for comment before publishing a revised version. However, DETR decided to use an abridged version before this consultation was complete. It should be borne in mind that this document has not, therefore, undergone the peer assessment which had been intended. To avoid unnecessary further work, the material is presented as it had been drafted for the GOMMMS Guidance document. The only modifications have been to change the chapter and paragraph numbers, and to remove the cross references to other parts of the Guidance document