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Open Access Publications from the University of California

Scholarly Works (7 results)

  • Thesis
  • Peer Reviewed
UC Irvine Electronic Theses and Dissertations (2021)

Online shopping has increased steadily over the past decade that has led to a dramatic increase in the demand for urban package deliveries. Crowdsourced delivery, or crowd shipping, has been proposed and implemented by logistics companies in response to the growth in package delivery business. Crowdsourced delivery is a delivery service in which logistics service providers contract delivery services from the public (i.e., non-employees), instead of providing delivery services exclusively with an in-house logistics workforce. This dissertation studies different types of urban last-mile crowdsourced delivery services and provides a taxonomy for crowdsourced package delivery. Urban package crowdsourced delivery can be categorized in terms of the way packages are delivered and the role/tasks of crowdsourced drivers. Given these two dimensions, this study identifies three types of urban package crowdsourced delivery, namely, crowdsourced time-based delivery, crowdsourced trip-based delivery, and crowdsourced shared-trip delivery. Crowdsourced time-based delivery drivers are paid for their idle time and work as sub-contractors. Crowdsourced trip-based delivery matches drivers with individual tasks and utilizes the drivers for specific delivery trips. The last type, crowdsourced shared-trip delivery utilizes the common segments of a crowdsourced personal vehicle trip to deliver packages. In this type, the package shares part of the driver trip. The literature formulates the crowdsourced delivery problem as a Vehicle Routing Problem (VRP) and proposes a variety of solution approaches. However, all the solution algorithms are limited to relatively small-scale problems. In addition, the factors that impact the efficiency and effectiveness of crowdsourced delivery have not been thoroughly analyzed. To bridge the gap in crowdsourced delivery and urban freight logistics, this dissertation provides an alternative formulation for the static crowdsourced shared-trip delivery problem and proposes a novel decomposition heuristic to solve the problem. The alternative formulation is based on the set partitioning problem. The novel decomposition heuristic handles packages that are served by shared personal vehicles (SPVs) and dedicated vehicles (DVs), separately. After that, the algorithm deploys a package switch procedure, which rearranges packages between SPVs and DVs. The dissertation discusses various algorithms employed to solve different sub-problems, such as the budgeted k-shortest path, large scale bi-partite matching, decision of package switching and vehicle routing. To validate the models and algorithms, this dissertation presents a numerical case study that uses the network of the City of Irvine, CA, USA. The results of the numerical study unveil interesting results that are valuable to both researchers and industrial practitioners. The results indicate that crowdsourced shared-trip delivery service can reduce total cost by between 20% to 50%, compared to a delivery service that exclusively uses its own dedicated vehicles and drivers. However, the results show that dedicated vehicles are still required since the shared vehicles are not able to serve all packages even with a considerably large set of candidate shared vehicles. Vehicle Miles Traveled (VMT) savings depend on the crowdsourced driver selection and their trip origins. The dissertation also analyzes and discusses important factors that impact the effectiveness of crowdsourced delivery. In particular, the dissertation includes sensitivity analysis results with respect to changes in the depot location and the willingness of shared vehicles to detour.

    Cover page: Planning and Operation of a Crowdsourced Package Delivery System: Models, Algorithms and ApplicationsCreative Commons 'BY-NC-ND' version 4.0 license
    • Peer Reviewed
    UC Irvine Previously Published Works (2020)
    Eco-driving strategies have been applied to smooth traffic flow and reduce greenhouse gas emissions along with air pollution. In this paper, we propose an eco-driving strategy to reduce traffic oscillation and smooth trajectories for connected vehicles following a moving bottleneck on a single-lane road. The eco-driving strategy, which leverages vehicle-to-vehicle (V2V) communications, designs advisory speed limits for each following vehicle through a control algorithm. The algorithm is based on the prediction of the following vehicle trajectories dictated by a moving bottleneck. The following vehicle trajectories are obtained by analytically solving the moving bottleneck problem in which the moving bottleneck speeds vary over time. In addition, the bounded acceleration rate is imposed in car-following behavior. The benefits of this strategy are demonstrated by applying it to four scenarios with different bottleneck movements. By simulating the scenarios with Newell’s car-following model with bounded acceleration and VT-Micro emission model, we find that both speed fluctuations and emissions are reduced with the algorithm in the scenarios in which the moving bottleneck has a constant speed, accelerates, decelerates and stops-and-goes. The results indicate that the proposed eco-driving algorithm can smooth traffic flow behind a moving bottleneck.
      Cover page: Eco-Driving Algorithm with a Moving Bottleneck on a Single-Lane RoadCreative Commons 'BY' version 4.0 license
      • Article
      • Peer Reviewed
      UC Irvine Previously Published Works (2021)
      Demand for electric vehicles (EVs), and thus EV charging, has steadily increased over the last decade. Studies suggest that fast-charging facilities are crucial for the EV market. However, there is limited fast-charging infrastructure in most parts of the world to support EV travel, especially long-distance trips. The goal of this study is to develop a stochastic dynamic simulation modeling framework of a regional system of EV fast-charging stations for real-time management and strategic planning (i.e., capacity allocation) purposes. To model EV user behavior, specifically fast-charging station choices, the framework incorporates a multinomial logit station choice model that considers station charging prices, expected wait times, and detour distances. To capture the dynamics of supply and demand at each EV fast-charging station, the framework incorporates a multi-server queueing model in the simulation. The study assumes that multiple fast-charging stations are managed by a single entity (public or private) and that the demand for these stations are interrelated (through the station choice model). To manage the system of stations, this study proposes and tests dynamic demand-responsive price adjustment (DDRPA) schemes based on station queue lengths. The study applies the modeling framework to a system of EV fast-charging stations in Southern California. The computational results indicate that DDRPA strategies are an effective mechanism to balance charging demand across fast-charging stations. Specifically, compared to the no DDRPA scheme case, the quadratic DDRPA scheme reduces average wait time by 26%, increases charging station revenue (and user costs) by 5.8%, while, most importantly, increasing social welfare by 2.7% in the base scenario. Moreover, the study also illustrates that the modeling framework can evaluate the allocation of EV fast-charging station capacity, to identify stations that require additional chargers and areas that would benefit from additional fast-charging stations.
      • 1 supplemental PDF
      Cover page: Dynamic modeling and real-time management of a system of EV fast-charging stationsCreative Commons 'BY' version 4.0 license
      • Article
      UCCONNECT (2017)
      Peer-to-peer (P2P) ridesharing is a relatively new concept that aims at providing a sustainable method for transportation in urban areas. This research is on the second phase of a sequence of projects that follows the previously funded UCConnect project titled “Promoting Peer-toPeer Ridesharing Services as Transit System Feeders”. In this phase, the study constructs a multimodal network, which includes P2P ridesharing, transit and city bike-sharing. The research develops schemes to provide travel alternatives, routes and information across multiple modes in the network. In addition, we develop a mobile application that demonstrates the research in the context of Los Angeles, CA, by using a combination of subway transit lines, proposed P2P ridesharing, and bikesharing to provide multi-modal itineraries to users. The Los Angeles Metro’s Red and Gold line subway rail and the downtown bike-share system are included in the network for a case study. The study includes a simulation of the operation of the combined system that provides travel alternatives during morning peak hours for multiple riders. The results indicate that a multi-modal network would expand the coverage of public transit. Ridesharing and bike-sharing could both act as transit feeders when properly designed in the system. Increased travel demand from the system can induce the problem that pick-up and drop-off demand in the bike system is not evenly distributed in space and time, which implies that bike redistribution should be introduced. We also develop algorithms to improve service level and reduce unsatisfied bike demand.
        Cover page: Designing a Transit-Feeder System Using Bikesharing and Peer-to-Peer Ridesharing
        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2018)
        Peer-to-peer (P2P) ridesharing is a relatively new concept that aims to provide a sustainable method for transportation in urban areas. Previous studies have demonstrated that a system that incorporates both P2P ridesharing and transit would enhance mobility. We develop schemes to provide travel alternatives, routes and information across multiple modes, which includes P2P ridesharing, transit, city bike-sharing and walking, within the network. This study includes a case study of the operation of the multimodal system that includes P2P ridesharing participants (both drivers and riders), the Los Angeles Metro Red line subway rail, and the Los Angeles downtown bike-share system. The study conducts a simulation, enhanced by an optimization layer, of providing travel alternatives to passengers during morning peak hours. The results indicate that a multi-modal network expands the coverage of public transit, and that ride- and bike-sharing could be effective transit feeders when properly designed and integrated into the transit system.
          Cover page: Designing a Transit-Feeder System using Multiple Sustainable Modes: Peer-to-Peer (P2P) Ridesharing, Bike Sharing, and WalkingCreative Commons 'BY' version 4.0 license
          • Article
          • Peer Reviewed
          UC Irvine Previously Published Works (2020)
          This paper addresses the problem of dynamically matching automated vehicles (AVs) to open traveler requests in a large-scale automated-mobility-on-demand (AMOD) simulation framework. While optimization-based matching strategies based on the linear assignment problem formulation significantly outperform simple heuristic strategies (e.g. nearest neighbor), the scalability of the assignment problem limits its applicability to large problem instances. This study proposes a fleet dispatching strategy to dynamically assign AVs to travelers that involves the assignment problem formulation but restricts the decision space to reduce computational time. First, we significantly trim the decision space via only considering the k-closest open requests around each idle vehicle or k-closest idle vehicles around each open request. Second, we only calculate point-to-point shortest paths for vehicles and travelers that are close in spatial proximity. For vehicles and travelers that are not close in proximity, we use zone-to-zone travel time estimates. This study embeds the proposed AV fleet dispatching strategy within Polaris-an agent-based transportation simulation modeling framework. Within Polaris, the restricted fleet dispatching strategy proposed in this significantly outperforms (i) existing large-scale strategies in terms of fleet performance and (ii) the unrestricted assignment problem strategy in terms of computational performance.
            Cover page: Effective and Efficient Fleet Dispatching Strategies for Dynamically Matching AVs to Travelers in Large-scale Transportation SystemsCreative Commons 'BY' version 4.0 license
            • Article
            Policy Briefs (2024)
            Microtransit is a mobility service that dynamically routes and schedules 6- to 20-seat vehicles to serve passengers within a defined region. Microtransit services are similar to ride-pooling services operated by Transportation Network Companies (e.g., Uber, Lyft); however, microtransit services are owned by cities or transit agencies. Integrating microtransit services with traditional fixed-route transit (FRT) has been touted as a means to attract more riders to public transit generally, improve mobility and sustainable transportation outcomes (e.g., reduce greenhouse gasses and local pollutants), and provide better accessibility to disadvantaged travelers. However, few academic studies have evaluated these claims. To address this gap, we surveyed California transit agencies that currently operate or recently operated microtransit services to obtain insights into integration challenges. We also developed an agent- and simulation-based modeling framework to evaluate alternative system designs for integrating FRT and microtransit in downtown San Diego and Lemon Grove, a suburban area in San Diego County.
              Cover page: Integrating Microtransit Service with Traditional Fixed-Route Transit Costs More but Greatly Improves Access to Jobs
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