A passenger-to-driver matching model for commuter carpooling: Case study and sensitivity analysis

For the transport sector, promoting carpooling to private car users could be an effective strategy over reducing vehicle kilometers traveled. Theoretical studies have verified that carpooling is not only beneficial to drivers and passengers but also to the environment. Nevertheless, despite carpooling having a huge potential market in car commuters, it is not widely used in practice worldwide. In this paper, we develop a passenger-to-driver matching model based on the characteristics of a private-car based carpooling service, and propose an estimation method for time-based costs as well as the psychological costs of carpooling trips, taking into account the potential motivations and preferences of potential carpoolers. We test the model using commuting data for the Greater London from the UK Census 2011 and travel-time data from Uber. We investigate the service sensitivity to varying carpooling participant rates and fee-sharing ratios with the aim of improving matching performance at least cost. Finally, to illustrate how our matching model might be used, we test some practical carpooling promotion instruments. We found that higher participant role flexibility in the system can improve matching performance significantly. Encouraging commuters to walk helps form more carpooling trips and further reduces carbon emissions. Different fee-sharing ratios can influence matching performance, hence determination of optimal pricing should be based on the specific matching model and its cost parameters. Disincentives like parking charges and congestion charges seem to have a greater effect on carpooling choice than incentives like preferential parking and subsidies. The proposed model and associated findings provide valuable insights for designing an effective matching system and incentive scheme for carpooling services in practice

Changing Car Culture: A Case Study at Binghamton University

Binghamton University has a parking problem fostered by the car culture of today. A change in car culture through the shift from single occupancy driving towards higher occupancy transit was identified as a possible solution. An online survey was used to acquire students\u27 opinions and thoughts on the issue. Its 824 responses highlighted variables that were grouped into five overarching themes: Convenience, Quality of Transportation System, Satisfaction with Parking, Comfort with Carpooling, and Perceived Benefits and Drawbacks, which were analyzed under different qualitative and quantitative methods to test for their effect on car culture. Qualitative analysis was conducted using R and SPSS to run Chi-square tests and linear regression models, whilst qualitative analysis was conducted using NVivo to run coding and word frequency queries. These results showed trends in student behavioral intentions, providing the understanding needed to promote initiatives to instigate car culture change and potentially reduce the parking problem

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

Understanding Commuter Patterns and Behavior: An Analysis to Recommend Policies Aimed at Reducing Vehicle Use

This study focused on the use of single occupancy vehicles by employee and student commuters at the University at Albany. The team conducted a review of the existing options for alternative transportation, developed GIS maps of commuting patterns, investigated the on-time performance of mass transit and created a survey to examine perceptions and barriers to using alternative transportation. The report includes a handbook for conducting a similar analysis at other institutions

SOLVING TRAFFIC CONGESTION FROM THE DEMAND SIDE

It is nowadays widely accepted that solving traffic congestion from the demand side is more important and more feasible than offering more capacity or facilities for transportation. Following a brief overview of evolution of the concept of Travel Demand Management (TDM), there is a discussion on the TDM foundations that include demand-side strategies, traveler choice and application settings and the new dimensions that ATDM (Active forms of Transportation and Demand Management) bring to TDM, i.e. active management and integrative management. Subsequently, the authors provide a short review of the state-of-the-art TDM focusing on relevant literature published since 2000. Next, we highlight five TDM topics that are currently hot: traffic congestion pricing, public transit and bicycles, travel behavior, travel plans and methodology. The paper closes with some concluding remarks

Sustainable Passenger Transportation: Dynamic Ride-Sharing

Ride-share systems, which aim to bring together travelers with similar itineraries and time schedules, may provide significant societal and environmental benefits by reducing the number of cars used for personal travel and improving the utilization of available seat capacity. Effective and efficient optimization technology that matches drivers and riders in real-time is one of the necessary components for a successful ride-share system. We formally define dynamic ride-sharing and outline the optimization challenges that arise when developing technology to support ride-sharing. We hope that this paper will encourage more research by the transportation science and logistics community in this exciting, emerging area of public transportation

DYNAMIC RIDESHARE OPTIMIZED MATCHING PROBLEM

This dissertation develops a Dynamic Rideshare Optimized Matching (DROM) model and solution that is aimed at identifying suitable matches between passengers requesting rideshare services with appropriate drivers available to carpool for credits and HOV lane privileges. DROM receives passengers and drivers' information and preferences continuously over time and maximizes the overall system performance subject to ride availability, capacity, rider and driver time window constraints, and detour and relocation distances while considering users' preferences. The research develops a spatial, temporal, and hierarchical decomposition solution strategy that leads to the heuristic solution procedure. Three-Spherical Heuristic Decomposition Model (TSHDM). Quality and validity tests for the TSHDM algorithm are done by comparison of results between the exact and implemented algorithm solutions and major sensitivity analyses using the technique of Regression Analysis on all of the related parameters in the model are conducted to thoroughly investigate the properties of the proposed model and solution algorithm. A case study is constructed to analyze the model and TSHDM behaviors on a road network of northwest metropolitan area of Baltimore city. The study shows that however DROM is a very complicated and challenging problem from both mathematical formulation and solution algorithm perspectives, it is possible to implement a dynamic rideshare system using appropriate technical tools and social networking media. Major sensitivity analysis conducted on several parameters and variables affecting the model shows that most influencing factors for the rate of success in the rideshare system are, in order of importance: number of participating drivers, number of stops, area size, and number of participating riders. The study also shows rate of success for the rideshare system is highly dependent to the matched routes connecting directly points of origin and destination for participating riders and also increasing the number of connections from one to two which requires two consecutive change of rides for a rider has the least impact on the rate of success