Towards privacy-driven design of a dynamic carpooling system

Dynamic carpooling (also known as instant or ad-hoc ridesharing) is a service that arranges one-time shared rides on very short notice. This type of carpooling generally makes use of three recent technological advances: (i) navigation devices to determine a driver’s route and arrange the shared ride; (ii) smartphones for a traveller to request a ride from wherever she happens to be; and (iii) social networks to establish trust between drivers and passengers. However, the mobiquitous environment in which dynamic carpooling is expected to operate raises several privacy issues. Among all the personal identifiable information, learning the location of an individual is one of the greatest threats against her privacy. For instance, the spatio-temporal data of an individual can be used to infer the location of her home and workplace, to trace her movements and habits, to learn information about her centre of interests or even to detect a change from her usual behaviour. Therefore, preserving location privacy is a major issue to be able to leverage the possibilities offered by dynamic carpooling. In this paper we use the principles of privacy-by-design to integrate the privacy aspect in the design of dynamic carpooling, henceforth increasing its public (and political) acceptability and trust.     

基于VANET叫车系统的路由算法研究

结合成熟的车载自组网技术和泛在的智能手持终端设备,提出一种去中心的动态叫车系统。乘客可以通过该系统随时发布叫车请求,并由车载自组网完成该请求的路由及车辆的匹配。同时,系统还提供了拼车功能,私家车也可通过该功能搭载顺路乘客。针对信息传输特点,重点研究一种基于效用值转发的路由算法,它根据车辆匹配成功概率计算效用值,并采用基于二分法的有限副本扩散策略,有效地避免信息盲目转发、减少网络负担。实验仿真结果表明,该系统的叫车成功率优于传统系统,基于效用的有限副本路由在网络开销、时延等方面均优于传统的传染路由和效用路由。     

Never drive alone: Boosting carpooling with network analysis

Carpooling, i.e., the act where two or more travelers share the same car for a common trip, is one of the possibilities brought forward to reduce traffic and its externalities, but experience shows that it is difficult to boost the adoption of carpooling to significant levels. In our study, we analyze the potential impact of carpooling as a collective phenomenon emerging from people׳s mobility, by network analytics. Based on big mobility data from travelers in a given territory, we construct the network of potential carpooling, where nodes correspond to the users and links to possible shared trips, and analyze the structural and topological properties of this network, such as network communities and node ranking, to the purpose of highlighting the subpopulations with higher chances to create a carpooling community, and the propensity of users to be either drivers or passengers in a shared car. Our study is anchored to reality thanks to a large mobility dataset, consisting of the complete one-month-long GPS trajectories of approx. 10% circulating cars in Tuscany. We also analyze the aggregated outcome of carpooling by means of empirical simulations, showing how an assignment policy exploiting the network analytic concepts of communities and node rankings minimizes the number of single occupancy vehicles observed after carpooling.     

An artificial bee colony algorithm with variable neighborhood search and tabu list for long-term carpooling problem with time window

Carpooling is an effective sharing economy mode that can increase utilization ratio of vehicle and relieve traffic pressure in cities. A carpooling system needs to provide feasible car-sharing solution under the constraint on time window of each passenger. Moreover, fairness is important to a long-term car sharing problem. A person will not willing to share his/her car if he/she has more contribution than others. In this paper, a long-term carpooling problem with time window is investigated for a group of people with a common destination. In order to guarantee fairness, a person can be chosen as a driver only for a limited number of consecutive days. An artificial bee colony algorithm combining variable neighbor search and tabu list (ABC-VNSTL) are proposed. We designed five neighbor search strategies: one-swapping strategy, all-swapping strategy, moving strategy, passenger to driver strategy, and solution exchange strategy. The experiments results demonstrate that ABC-VNSTL can obtain better solution quality than other six algorithms in the literature. As the number of participants increases, the advantages of ABC-VNSTL increase. In addition, ABC-VNSTL algorithm is more efficient than the compared algorithms under the condition of achieving same solution quality.     

The Vanpool Assignment Problem: Optimization models and solution algorithms

We present optimization models and solution algorithms for the Vanpool Assignment Problem. A vanpool is typically a group of 9-15 passengers who share their commute to a common target location (typically an office building or corporate campus). Commuters in a vanpool drive from their homes to a park-and-ride location where they board a van and ride together to the target location; at the end of the work day they ride together back to the park-and-ride location. The Minimum Cost Vanpool Assignment Model (MCVAM) developed in this study is motivated by a program offered by Gulfstream Aerospace, a large employer in the Dallas/Fort-Worth area, Dallas Area Rapid Transit (DART), and Enterprise Rent-A-Car. Our MCVAM imposes constraints on the capacity of each van and quality-of-service constraints on the cost and travel time involved in joining a vanpool. The goal of the MCVAM is to minimize the total cost of a one-way trip to the target location for all employees (including those employees who opt-out of the program and choose not to join a vanpool). To the best of our knowledge, this is the first mathematical programming model proposed for the standard (one-stop) Vanpool Assignment Problem. The MCVAM models the current practice in vanpooling of using one park-and-ride location per vanpool. We also present a Two-Stop MCVAM (TSMCVAM) that offers significant cost savings compared to the MCVAM with little or no increase in trip times for most passengers by allowing vanpools to stop at a second park-and-ride location. We present heuristics for the TSMCVAM which are shown in a computational study to find solutions with optimality gaps ranging from 5% to 10% in CPU times ranging from 1 to 15 min for problem instances with up to 600 employees and 120 potential park-and-ride locations.