Impact of high occupancy vehicle lanes on carpooling behavior

High occupancy vehicle lanes have become an integral part of regional transportation planning. Their purpose is to increase ridesharing by offering a travel time advantage to multiple occupant vehicles. This paper examines the extent to which an HOV facility increases ridesharing. Using data from the Route 55 HOV facility in Orange Country, California, changes in the carpooling rate on Route 55 are compared to that of a control group of freeway commuters. The analysis shows that the carpooling rate among peak period commuters, and particularly those who use the entire length of the facility, has increased. However, there has been no significant increase in ridesharing among the entire population of Route 55 commuters. Results suggest that barriers to increased ridesharing are formidable, that travel time savings must be large in order to attract new carpoolers, and that further increases in capooling will likely require development of extensive HOV lane systems.     

The influence of employer ridesharing programs on employee mode choice

Employer ridesharing programs and employee mode choice were analyzed using Southern California data. Problems in estimating the costs and benefits of employer ridesharing programs were identified. Surveyed firms used a wide variety of information to estimate employee mode split internally. Virtually all surveyed firms offered free or subsidized parking to some or all of their employees. Few responding firms estimated the cost of providing employee parking accurately, if at all. Despite these significant data limitations, factors influencing firm choice of employer ridesharing program components were identified. The influence of employer ridesharing programs on employee mode choice was modeled using weighted least squares logit regression analysis. Firm size was foung to be the single most important variable identified in the analysis. Larger firms were more likely to offer ridesharing incentives to their empolyees, and to report direct employer benefits from ridesharing. Alternative work hours hindered the formation of ridesharing arrangements in some cases. Relatively few firms promoted ridesharing on a purely voluntary basis. A private market for employer ridesharing services was found to exist, however. Personalized matching assistance may be a critical factor in developing more effective employer ridesharing programs. Parking pricing and supply control measures probably would have a larger impact on employee mode split overall. Parking management faces severe obstacles in implementation, some of which might be overcome through the more extensive provision of ridesharing services, such as personalized matching assistance. to employees at specific employment sites by their employers.     

Ridesharing: The state-of-the-art and future directions

Although ridesharing can provide a wealth of benefits, such as reduced travel costs, congestion, and consequently less pollution, there are a number of challenges that have restricted its widespread adoption. In fact, even at a time when improving communication systems provide real-time detailed information that could be used to facilitate ridesharing, the share of work trips that use ridesharing has decreased by almost 10% in the past 30 years.In this paper we present a classification to understand the key aspects of existing ridesharing systems. The objective is to present a framework that can help identify key challenges in the widespread use of ridesharing and thus foster the development of effective formal ridesharing mechanisms that would overcome these challenges and promote massification.     

网约共享出行研究综述

网约共享出行是智慧城市交通系统的重要组成部分,作为新兴的移动互联出行方式,产生 了海量庞杂、异质多源、大尺度时空关联的交通大数据,蕴含能够描述复杂交通系统供需态势的 丰富信息。从网约共享出行行为机理、平台管理优化、政府监管政策、系统仿真优化等4个方面, 综述了国内外网约共享出行研究的基础理论前沿和交通运输管理实践成果,归纳总结了其中存 在的问题。通过移动互联交通大数据,分析网约车乘客和司机的出行行为影响因素、特征辨识及 外部性,追踪城市个体和群体的出行行为演变规律,揭示网约共享出行系统供需平衡和网络均衡 机理。研究解决网约共享出行供需的时空效应及短时预测问题,优化网约共享出行平台定价策 略,提高平台匹配和调度效率,实现供需时空资源的优化配置。利用智能体仿真、基于活动的仿 真、数据驱动的仿真等技术手段对理论结果进行模拟推演和优化验证,为政府制定相关监管政策 和平台优化运营管理策略提供理论依据和工具支持。并面向复杂动态移动互联环境,展望了亟 须开展的若干重点研究方向。     

A traffic assignment model for a ridesharing transportation market

A nascent ridesharing industry is being enabled by new communication technologies and motivated by the many possible benefits, such as reduction in travel cost, pollution, and congestion. Understanding the complex relations between ridesharing and traffic congestion is a critical step in the evaluation of a ridesharing enterprise or of the convenience of regulatory policies or incentives to promote ridesharing. In this work, we propose a new traffic assignment model that explicitly represents ridesharing as a mode of transportation. The objective is to analyze how ridesharing impacts traffic congestion, how people can be motivated to participate in ridesharing, and, conversely, how congestion influences ridesharing, including ridesharing prices and the number of drivers and passengers. This model is built by combining a ridesharing market model with a classic elastic demand Wardrop traffic equilibrium model. Our computational results show that (i) the ridesharing base price influences the congestion level, (ii) within a certain price range, an increase in price may reduce the traffic congestion, and (iii) the utilization of ridesharing increases as the congestion increases. Copyright © 2014 John Wiley & Sons, Ltd.     

Applying a structured simulation-based methodology to assess carpooling time–space potential

Abstract

Increasing urban traffic congestion calls for the study of alternative measures. One such measure is carpooling, a system in which a person shares his private vehicle with one or more people in a commuter trip. In principle, this system could lead to potentially significant reductions in the use of private vehicles; however, in practice it has achieved limited success. In this paper, we apply a simulation-based methodology that uses aggregated data from commuter trips in an urban area to create compatible and feasible random trips. These are then analyzed through a heuristic process recursively to find grouping possibilities, thus producing indicators of carpooling potential such as the percentage of matched trips. Using this methodology, simulations are run for the Lisbon Metropolitan Area (Portugal) and results show that an increase in the number of participants in a carpooling scheme will only increase the probability of matching up to a certain point, and that this probability varies significantly with time–space trip attributes.     

Meeting points in ridesharing: A privacy-preserving approach

Nowadays, problems of congestion in urban areas due to the massive usage of cars, last-minute travel needs and progress in information and communication technologies have fostered the rise of new transportation modes such as ridesharing. In a ridesharing service, a car owner shares empty seats of his car with other travelers. Recent ridesharing approaches help to identify interesting meeting points to improve the efficiency of the ridesharing service (i.e., the best pick-up and drop-off points so that the travel cost is competitive for both driver and rider). In particular, ridesharing services, such as Blablacar or Carma, have become a good mobility alternative for users in their daily life. However, this success has come at the cost of user privacy. Indeed in current’s ridesharing services, users are not in control of their own data and have to trust the ridesharing operators with the management of their data.In this paper, we aim at developing a privacy-preserving service to compute meeting points in ridesharing, such that each user remains in control of his location data. More precisely, we propose a decentralized architecture that provides strong security and privacy guarantees without sacrificing the usability of ridesharing services. In particular, our approach protects the privacy of location data of users. Following the privacy-by-design principle, we have integrated existing privacy enhancing technologies and multimodal shortest path algorithms to privately compute mutually interesting meeting points for both drivers and riders in ridesharing. In addition, we have built a prototype implementation of the proposed approach. The experiments, conducted on a real transportation network, have demonstrated that it is possible to reach a trade-off in which both the privacy and utility levels are satisfactory.     

考虑合乘的共享自动驾驶汽车选择行为实证分析

共享自动驾驶汽车(Shared Autonomous Vehicles,SAV)是自动驾驶汽车和共享经济相结合的产物,为人们提供了一种新型的出行方式. 为探究出行者在考虑合乘的SAV与私家车或公共交通之间的选择偏好,实施了SAV选择意愿调查,并分析了考虑合乘的SAV的潜在用户特征. 基于问卷调查所得有效数据,采用K-Means 聚类法划分了历史出行模式,利用因子分析对性格态度特征进行了分类. 分别对有无私家车人群建立解释变量的参数服从不同分布的混合Logit 模型,并对参数标定结果进行对比分析. 研究结果表明,出行方式特性非常显著地影响出行者方式选择行为,性格态度特征是影响出行者选择考虑合乘的SAV出行方式的显著因素,且其显著性明显高于性别、年龄等社会经济属性.