Dynamic carpooling in urban areas: design and experimentation with a multi-objective route matching algorithm

This paper focuses on dynamic carpooling services in urban areas to address the needs of mobility in real-time by proposing a two-fold contribution: a solution with novel features with respect to the current state-of-the-art, which is named CLACSOON and is available on the market; the analysis of the carpooling services performance in the urban area of the city of Cagliari through emulations. Two new features characterize the proposed solution: partial ridesharing, according to which the riders can walk to reach the driver along his/her route when driving to the destination; the possibility to share the ride when the driver has already started the ride by modeling the mobility to reach the driver destination. To analyze which features of the population bring better performance to changing the characteristics of the users, we also conducted emulations. When compared with current solutions, CLACSOON allows for achieving a decrease in the waiting time of around 55% and an increase in the driver and passenger success rates of around 4% and 10%, respectively. Additionally, the proposed features allowed for having an increase in the reduction of the CO2 emission by more than 10% with respect to the traditional carpooling service

Optimization of vehicular networks in smart cities: from agile optimization to learnheuristics and simheuristics

Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an ‘Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.Peer ReviewedPostprint (published version

A two-stage approach to ridesharing assignment and auction in a crowdsourcing collaborative transportation platform.

Collaborative transportation platforms have emerged as an innovative way for firms and individuals to meet their transportation needs through using services from external profit-seeking drivers. A number of collaborative transportation platforms (such as Uber, Lyft, and MyDHL) arise to facilitate such delivery requests in recent years. A particular collaborative transportation platform usually provides a two sided marketplace with one set of members (service seekers or passengers) posting tasks, and the another set of members (service providers or drivers) accepting on these tasks and providing services. As the collaborative transportation platform attracts more service seekers and providers, the number of open requests at any given time can be large. On the other hand, service providers or drivers often evaluate the first couple of pending requests in deciding which request to participate in. This kind of behavior made by the driver may have potential detrimental implications for all parties involved. First, the drivers typically end up participating in those requests that require longer driving distance for higher profit. Second, the passengers tend to overpay under a competition free environment compared to the situation where the drivers are competing with each other. Lastly, when the drivers and passengers are not satisfied with their outcomes, they may leave the platforms. Therefore the platform could lose revenues in the short term and market share in the long term. In order to address these concerns, a decision-making support procedure is needed to: (i) provide recommendations for drivers to identify the most preferable requests, (ii) offer reasonable rates to passengers without hurting driver’s profit. This dissertation proposes a mathematical modeling approach to address two aspects of the crowdsourcing ridesharing platform. One is of interest to the centralized platform management on the assignment of requests to drivers; and this is done through a multi-criterion many to many assignment optimization. The other is of interest to the decentralized individual drivers on making optimal bid for multiple assigned requests; and this is done through the use of prospect theory. To further validate our proposed collaborative transportation framework, we analyze the taxi yellow cab data collected from New York city in 2017 in both demand and supply perspective. We attempt to examine and understand the collected data to predict Uber-like ridesharing trip demands and driver supplies in order to use these information to the subsequent multi-criterion driver-to-passenger assignment model and driver\u27s prospect maximization model. Particularly regression and time series techniques are used to develop the forecasting models so that centralized module in the platform can predict the ridesharing demands and supply within certain census tracts at a given hour. There are several future research directions along the research stream in this dissertation. First, one could investigate to extend the models to the emerging concept of Physical Internet on commodity and goods transportation under the interconnected crowdsourcing platform. In other words, integrate crowdsourcing in prevalent supply chain logistics and transportation. Second, it\u27s interesting to study the effect of Uber-like crowdsourcing transportation platforms on existing traffic flows at the various levels (e.g., urban and regional)

Exploration of the Current State and Directions of Dynamic Ridesharing

Dynamic ridesharing (DRS) is an emerging transportation service based on the traditional concept of shared rides. DRS makes use of web-based real-time technologies to match drivers with riders. Enabling technologies include software platforms that operate on mobile communication devices and contain location-aware capabilities including Global Positioning Systems (Agatz, Erera, Savelsberg, & Wang, 2012). The platforms are designed to provide ride-matching services via smartphone applications differing from early systems that used non-real time services such as internet forums, or telecommunications, where responses were not immediate. The study of DRS is important when considering its role as an emerging transportation demand management strategy. DRS reduces travel demand on singleoccupancy vehicles (SOVs) by filling vehicle seats that are typically left vacant. The most recent statistics of vehicle occupancy rates were measured in 2009 by the National Household Travel Survey (NHTS), conducted by the U.S. Department of Transportation. According to the NHTS, the 2009 occupancy rate for all purposes was a meager 1.67 persons per vehicle (Federal Highway Administration, 2015). Vehicle occupancy rates examined against the total of all registered highway vehicles in the U.S. as of 2012, calculated at 253,639,386 (Bureau of Transportation Statistics, 2015), reveals the magnitude of the impact of SOVs. Left unattended, the ramifications for environmental outcomes is substantial. Among the major energy consuming sectors, transportation\u27s share is largest in terms of total CO2 emissions at 32.9% (Davis, Diegel, & Boundy, 2014, p. 11-15). DRS offers promise to fill empty vehicle seats. Evidence indicates that specific demographic subgroups are inclined to use DRS services. For example, data suggest that the subgroup of 18 to 34-year-olds, the so-called millennials , have negative attitudes towards private car ownership unlike previous age groups (Nelson, 2013). Data collected for this study revealed that the millennial subgroup represents half of all DRS users. Millennials also revealed they tended to use DRS more than other subgroups to replace a private vehicle. Further research is needed to determine if the trend towards DRS by 18 to 34-year-olds represents current economic factors or a fundamental cultural shift away from the SOV transportation model

Quantifying the benefits of vehicle pooling with shareability networks

Taxi services are a vital part of urban transportation, and a considerable contributor to traffic congestion and air pollution causing substantial adverse effects on human health. Sharing taxi trips is a possible way of reducing the negative impact of taxi services on cities, but this comes at the expense of passenger discomfort quantifiable in terms of a longer travel time. Due to computational challenges, taxi sharing has traditionally been approached on small scales, such as within airport perimeters, or with dynamical ad-hoc heuristics. However, a mathematical framework for the systematic understanding of the tradeoff between collective benefits of sharing and individual passenger discomfort is lacking. Here we introduce the notion of shareability network which allows us to model the collective benefits of sharing as a function of passenger inconvenience, and to efficiently compute optimal sharing strategies on massive datasets. We apply this framework to a dataset of millions of taxi trips taken in New York City, showing that with increasing but still relatively low passenger discomfort, cumulative trip length can be cut by 40% or more. This benefit comes with reductions in service cost, emissions, and with split fares, hinting towards a wide passenger acceptance of such a shared service. Simulation of a realistic online system demonstrates the feasibility of a shareable taxi service in New York City. Shareability as a function of trip density saturates fast, suggesting effectiveness of the taxi sharing system also in cities with much sparser taxi fleets or when willingness to share is low.Comment: Main text: 6 pages, 3 figures, SI: 24 page