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Automated taxis ’ dial-a-ride problem with ride-sharing considering congestion-based dynamic travel times
Abstract
In this paper, we study the dial-a-ride problem of ride-sharing automated taxis (ATs) in an urban road network, considering the traffic congestion caused by the ATs. This shared automated mobility system is expected to provide a seamless door-to-door service for urban travellers, much like what the existing transportation network companies (TNC) do, but with decreased labour cost and more flexible relocation operations due to the vehicles’ automation. We propose an integer non-linear programming (INLP) model that optimizes the routing of the ATs to maximize the system profit, depending on dynamic travel times, which are a non-linear function of the ATs’ flows. It is important to involve traffic congestion in such a routing problem since for a growing number of ATs circulating in the city their number will lead to delays. The model is embedded within a rolling horizon framework, which divides a typical day into several horizons to deal with the real-time travel demand. In each horizon, the routing model is solved with the demand at that interval and assuring the continuity of the trips between horizons. Nevertheless, each horizon model is hard to solve given its number of constraints and decision variables. Therefore, we propose a solution approach based on a customized Lagrangian relaxation algorithm, which al- lows identifying a near-optimal solution for this difficult problem. Numerical experiments for the city of Delft, The Netherlands, are used to demonstrate the solution quality of the proposed algorithm as well as obtaining insights about the AT system performance. Results show that the solution algorithm can solve the proposed model for hard instances. Ride-sharing makes the AT system more capable to provide better service regarding delay time and the number of requests that can be attended by the system. The delay penalty on the profit objective function is an effective control parameter on guaranteeing the service quality while maintaining system prof- itability.
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... Tani et al. [18] constructed a model which also considers carpooling. On the other hand, Liang et al. [19] formulated the DARP as an integer nonlinear programming by applying a dynamic traffic assignment and solved it approximately by the Lagrangian relaxation method. Also, Fan et al. [20] formulated a similar problem to that of Liang et al. [19] and approximately solved it by performing linearization of variables. ...
... On the other hand, Liang et al. [19] formulated the DARP as an integer nonlinear programming by applying a dynamic traffic assignment and solved it approximately by the Lagrangian relaxation method. Also, Fan et al. [20] formulated a similar problem to that of Liang et al. [19] and approximately solved it by performing linearization of variables. These models can express the dynamic nature of traffic flows and provide time-varying travel times. ...
... (12) is about in-vehicle time, and it ensures that the in-vehicle time of each user is not shorter than the travel time needed to move directly from one's boarding node to the corresponding alighting node by a vehicle and is not longer than the maximum invehicle time. The in-vehicle time of each user is defined as: (13)- (19) are incorporated to the DARP. Here, y rs ij is defined by introducing the Address Book Mapping as: ...
Shared autonomous vehicles are anticipated to be adopted widely in the future and have prompted the development of various models for planning their operations. Existing models fall into three primary categories: micro-simulations, routing problems such as the Dial-a-Ride problem (DARP) with fixed travel times, and routing problems involving dynamic traffic assignment. However, none of these provide detailed input/output corresponding to each vehicle’s operation and theoretically accounting for congestion effects in the network. This paper introduces a novel approach that combines the DARP with a static user equilibrium traffic assignment model. Consequently, the DARP in this study is formulated as a mixed-integer nonlinear problem with equilibrium constraints. Finding an exact optimal solution to a nonlinear and nonconvex problem, such as the DARP addressed in this study, is generally regarded as a challenging task. This study proposes an algorithm for the DARP that finds a solution by repeatedly solving problems formulated using linearly approximated equilibrium link flows. Finally, numerical calculations are conducted to assess the model’s validity.
... They take into account the impact of congestion in determining the optimal trip assignment and dynamic routing of AVs. Expanding on this theme, Liang et al. (2020) delve into a dial-a-ride problem involving ride-sharing in light of the traffic congestion caused by the routing of a large number of AVs. Fan et al. (2022) investigate the heterogeneous fleet sizing and vehicle routing problem for an on-demand mobility service provider envisioning a progressive expansion of AVs-only zones. ...
... This dataset provides the daily mobility information of a sample of residents, including but not limited to the origin, destination, departure time, arrival time, transport mode, etc. It has been used previously (Correia and Van Arem, 2016;Liang et al., 2020) to study the future mobility system with AVs in urban networks. However, this dataset does not have a large sample of trips for this city if we focus on just one hour. ...
... Delay penalty which is set to 0.2 euro/min (Liang et al., 2020). ...
Shared automated vehicles (SAV) are expected to benefit the sustainable development of urban regions and alleviate the negative impacts brought by the increasing number of private cars. In this paper, we envision a future scenario where non-pooled SAVs replace private cars and provide public on-demand mobility services to satisfy the mobility needs of a city's residents. To help service providers make profitable fleet sizing and management decisions, we develop a mixed-integer non-linear programming model that considers the congestion effects and the mode choice of urban travellers in different income classes, between SAVs and bicycles. Our model optimises both strategic decisions (fleet size, initial fleet distribution, and service quality level) and operational decisions (trip assignment, vehicle routing, parking, and relocation). Travellers' preference for both transport modes is described through a binary logit model and congestion effects are described by dynamically varying travel times with respect to traffic flow in a non-linear fashion. In addition, we investigate two types of accept/reject mechanisms (mandatory vs. non-mandatory acceptance) which lead to an endogenously determined acceptance rate that can affect travellers' willingness to use SAV services. The computational challenge posed by the non-linear and non-convex nature of the model is addressed through reformulation and the use of outer-inner approximation methods combined with a breakpoint generation algorithm. We demonstrate the effectiveness of our proposed method in a case study of the city of Delft in The Netherlands, as well as a scaling analysis on three toy networks with various sizes and demand profiles. A sensitivity analysis of key parameters is carried out to assess system performance. Computational results indicate that fleet sizing decisions are influenced not only by the population's geographical distribution and land use patterns but also by the pricing strategy, unit operating costs of the SAV fleet, network congestion level, and traveller behaviour. When the price rate of using SAVs is low, the fleet sizing decisions can also be influenced by the trip accept/reject mechanism and the travellers' sensitivity to the service quality level. In addition, a low price of SAV service will attract more users but may not necessarily bring a higher profit because of the increased traffic congestion. ✩
... The DVRPRTTs studied in the literature are also motivated by different applications, including freight pickup and delivery (Güner et al., 2017;Rifki et al., 2020), dial-a-ride with ride-sharing (Liang et al., 2020), drone-assisted parcel delivery (Liu et al., 2022), etc. Almost all DVRPRTTs incorporate stochastic travel time information and have service time windows associated with customers. Lorini et al. (2011) and Respen et al. (2019) evaluate the diversion opportunities in DVRPRTTs, while Güner et al. (2017) and Vodopivec and Miller-Hooks (2017) develop proactive waiting strategies to exploit the dynamic traffic information. ...
... We recall that random travel times are also considered in some DPDPs (see Table 5), but these problems mostly do not incorporate stochastic information. Liang et al. (2020) solve their DVRPRTT in a rolling horizon reoptimization framework with equidistant decision epochs, and propose a Lagrangian relaxation algorithm to reoptimize the sequential SVRPs (modeled as integer nonlinear programs). The other DVRPRTTs are mostly modeled as MDPs. ...
... To solve these MDPs, Toriello et al. (2014) and Yu and Yang (2019) develop approximate linear programming approaches; Kim et al. (2016) propose rollout-based approaches to avoid curses of dimensionality; Köster et al. (2018) introduce a heuristic policy which aims at minimizing the expected travel time and avoiding the potentially critical areas in the city; Liu et al. (2022) develop a reinforcement-learning approach based on deep Q-network and advance actor-critic algorithms. Most DVRPRTT instances solved in the recent literature are based on real-world road networks (Kim et al., 2016;Güner et al., 2017;Köster et al., 2018;Liang et al., 2020;Liu et al., 2022), while Toriello et al. (2014), Respen et al. (2019), and Yu and Yang (2019) generate instances randomly or based on SVRP benchmarks. ...
... While the many-to-many TRS approach has received much attention in the literature (Mourad et al., 2019;Liang et al., 2020;Wang and Li, 2021;Ting et al., 2021) considerably less attention has been paid to one-to-one TRS systems. ...
... Due to TRS's 135 high complexity, mainly caused by the routing part of the problem, only a few exact solution methods have been proposed. Santi et al. (2014), Alonso-Mora et al. (2017) and Liang et al. (2020) develop methods based on a rolling horizon approach where an optimization model is used to periodically determine optimal taxi assignments and routes for all collected requests during a specified 140 time window. Wang et al. (2018) and Kucharski and Cats (2020) also consider a rolling horizon approach, albeit for P2P ridesharing. ...
... Moreover, the problem of home delivery is modeled in [122] as an DVRP with Linear Programming (LP) model in order to minimize the travel cost. Furthermore, the problem of automated taxis in [75], Taxi and ride-sharing in [113]. Further, several algorithms have been developed for this problem, such as: [23,51,58,79,80,84,100,108,111,123]. ...
... Real-life routing problems are prevalent in several areas, including taxi cabs, bus routing, bike-sharing systems, and courier services. For taxi cabs, the policies used to assign customers to taxis vary from company to company, and taxis can service more than one customer at the same time [75]. The problem of dynamic ride-sharing and taxi-sharing was studied in [113], with the aim of maximizing the number of served requests and minimizing travel costs. ...
The advancement of communication and information technologies has had a significant impact on the logistics industry and has given rise to new challenging transportation requirements. Indeed, the transportation of goods under a dynamic environment involves orders modification or canceling over time. These situations are modeled by the dynamic vehicle routing problem (DVRP), aiming to determine the most optimized routes, by taking into consideration whatever changes occur. Solving DVRP is challenging, as it necessitates efficient adaptation to changes in demand and other factors. This chapter presents a comprehensive overview of the current state of the art in DVRP within the context of supply chain management. To achieve this, we have surveyed 131 articles published from 2017 until the first quarter of 2023. First, we define the problem and describe its main features and challenges. We, also, pointed out the most striking differences between the static and dynamic transportation. Next, we review the main DVRP variants studied in the literature. Then, we highlighted real-world applications in this field. Finally, we discussed future trends and directions in DVRP research.
... The exclusion of privately-owned AVs is motivated by two primary factors. Firstly, numerous researchers envision a future where AVs are mainly used through sharing and pooling options integrated into public transport, rather than being privately owned (Liang et al., 2020;Stoiber et al., 2019); secondly, we anticipate that the overall number of privately-owned AVs will likely be relatively small compared to the number of ATs. This projection is attributed to the expected high cost of AVs and the prevailing trend of favouring public transport and active modes of transport in cities, thereby limiting private vehicle ownership (Nieuwenhuijsen & Khreis, 2016;UITP, 2017). ...
... represents the drop-off delay penalty, which is 0.2 euros/min based on (Liang et al., 2020). is the travel time related cost for PVs which is set to 9 euros/h based on (Kouwenhoven et al., 2014). ...
... Thus each transport unit will have its associated array of features z k i ∈ R d k . Moreover, it is possible to consider other planning schemes, such as product collection rather than delivery, or both (Steever et al., 2019), routes that are not closed (Liang et al., 2020), multiple depots (Bolanos et al., 2018), divisible demands , and so on, including combining some of the mentioned ones, so that the formulation reflects the real conditions faced by transportation systems. These factors generate the conception of variants that make up a large family of VRPs. ...
... However, in real-world applications, this condition is rarely met. On the contrary, real transportation systems tend to present constant changes and updates, such as in ride-hailing (Jiao et al., 2021), crowdsourced (Ahamed et al., 2021), and ride-sharing (Liang et al., 2020) business models; home food delivery services ; technical or health assistance services , to mention a few examples. Psaraftis (1980) proposed that the input data in VRPs have two dimensions: evolution and information quality. ...
... Compared with traditional fixed-route transit, FRT is more flexible because it allows the bus to deviate from the base route and provide door-todoor services [9]. Also, such a mode is more economical when offering door-to-door services due to the high capacity compared with dial-a-ride systems or taxis [10]. ...
... The final survey referred to two main points: (1) Sociodemographic characteristics of respondents; (2) Observed variables of respondents. The study selected six psychological latent factors (i.e., comfort, flexibility, perceived barriers, personal barriers, subjective evaluation, and use willingness) based on previous and mature researches in the field of TAM and TPB [10,27,28]. 18 observed variables were defined to reflect psychological latent factors on FRT by the Cattell's Scree Plot method [25,29]. ...
Flex-route transit (FRT) has significant advantages in low-demand areas. Existing studies have focused on practical experience, strategic planning, and operational planning. Few studies have addressed the effect of sociodemographic and psychological latent characteristics on the acceptance of FRT. This study aims at exploring the effect of sociodemographic and psychological latent characteristics on FRT acceptance. To finish the goal, a household survey is conducted from April to May 2020 in Nanjing, China. The survey includes sociodemographic characteristics and observed variables of individuals. Firstly, the study extracts six psychological latent characteristics to reflect individuals’ attitudes based on previous and mature researches in the field of technology acceptance model (TAM) and theory of planned behavior (TPB). Then, a multiple indicators and multiple causes (MIMIC) is applied to calculate six psychological latent characteristics. Finally, an integrated model, consisting of the MIMIC and a binary logit model (BLM), is applied to match sociodemographic and psychological latent characteristics. The BLM with sociodemographic characteristics is developed as the reference model to compare the effects of psychological latent characteristics. Results show that psychological latent factors play a significant role in estimating the effect on FRT acceptance. Results of the integrated model show that the parameter of car is -0.325, displaying individuals with private cars are more reluctant to use FRT. Therefore, restricting private cars is an effective measure to facilitate FRT. Improving flexibility (0.241) is a significant measure to facilitate FRT. Findings are expected to facilitate decision-making of transport planners and engineers, and therefore enhance the service of the FRT system.
... Many variants of the DARP were introduced in the past few years. For example, considering real-time demands, Liang et al. (2020) presented a variant aiming to the optimization of automated-taxis-sharing in an urban road network. Masmoudi et al. (2019) described a DARP based on the use of a mixed fleet, including both heterogeneous conventional and alternative fuel vehicles, in which a vehicle is allowed to refuel from a fuel station to eliminate the risk of running out of fuel during its service. ...
... As classically done in the VRP related literature, Variable Neighborhood Search and Variable Neighborhood Descent are widely used (Parragh et al., 2010(Parragh et al., , 2015Molenbruch et al., 2017;Henke et al., 2015). Alternative solution approaches were based on other well-established techniques, such as Tabu Search (Beaudry et al., 2008;Oppen and Løkketangen, 2008), ILS (Malheiros et al., 2021), Large Neighborhood Search (Masmoudi et al., 2019), or Lagrangian relaxation (Liang et al., 2020). ...
... The benefits of adopting SAVs in ride-hailing services are obvious. Firstly, the cost of using SAVs would exclude the driver labour costs, thus potentially lowering the overall expenses for consumers compared to traditional ride-hailing services (Liang et al., 2020;Wei et al., 2024). Secondly, SAVs can reduce the inefficient utilization of privately owned AVs, such as empty vehicle miles (Liu et al., 2020a), and be optimized through a central intelligent dispatching system to improve productivity (Kim et al., 2022;Zeng et al., 2023). ...
Shared autonomous vehicles (SAVs) are reshaping the mobility system, with ride-hailing services being regarded as a pioneering application market. Despite its benefits, SAVs inevitably exacerbate the competition for limited curb space, which is already a scarce resource. Designated pick-up and drop-off sites, if reasonably planned, present a promising solution to this challenge. In this paper, we propose a novel network-based data-driven approach to optimize PUDO locations for SAVs. Network kernel density estimation (NKDE) is used to measure the PUDO demand along the road network based on historical ride-hailing data. The results are fed into a maximal coverage location problem (MCLP) model to optimize the spatial distribution of PUDO sites by maximizing the demand they can cover. The spatial optimization model is used both prescriptively and descriptively, assessing transport policies and enhancing the operations of SAVs. Results show that our model outperforms the baseline models. We also reveal that a suitable PUDO site density is about 20/10 km ² , with denser sites in bustling urban centre and sparser sites in suburbs. The framework is highly generalizable and reproducible and can be extended to future studies.
... The purpose of a congestion charge is to discourage unnecessary trips and reduce traffic congestion in heavily congested areas. Remarkably, the issue of traffic congestion would lead to delays in taxi services ( Liang et al., 2020 ). Thus, the congestion effects and the associated pricing should be captured wisely by the taxi industry given its societal impact on taxi passengers and the operational cost concern of taxi operators ( Li et al., 2022 ). ...
... Agatz et al. [2] proposed a classification scheme for dynamic ride-sharing based on matching patterns, which includes single driver and single rider matching, single driver and multiple riders matching, multiple drivers and single rider matching, and multiple drivers and multiple riders matching. Additionally, dynamic ride-sharing can generally be categorized into hitchhiking mode [3][4][5] or taxi mode [6][7][8] based on service characteristics. In hitchhiking mode, the driver and rider post their respective journeys through a ride-sharing service platform to maximize the sharing distance between the driver and rider. ...
Ride-sharing has transformed people’s travel habits with the development of various ride-sharing platforms, which can enhance the utilization of transportation resources, alleviate traffic congestion, and reduce carbon emissions. However, the development of a general and efficient matching framework is challenging due to the dynamic real-time conditions and uncertainty of ride-sharing problems in the real world. Additionally, previous research has identified limitations in terms of model practicability and algorithmic solution speed. To address these issues, a two-stage dispatching approach for one-to-many ride-sharing with sliding time windows is proposed. The dynamic ride-sharing problem is formally defined, and an integer programming model is constructed to solve it. A multi-rider distance and time constraint algorithm uses a distance matrix and sliding time windows to preprocess data before matching is proposed, thereby optimizing data quality and improving computational efficiency. The ride-sharing process is divided into a reservation order matching stage based on path similarity and a real-time order matching stage based on path distance degree. A two-stage collaborative mechanism is designed to guide the collaboration of the two stages. Furthermore, numerical experiments are conducted using two real-world datasets from developing and developed country regions to verify the efficiency and practicability of the proposed approach.
... Many introducing researchers focus on developing ride-sharing algorithms to make them more practical; as an example, (Alonso-Mora, Samaranayake et al. 2017) presented a mathematical model for real-time high-capacity ridesharing that scales to large numbers of travelers and journeys and generates optimal routes based on online demand and vehicle location dynamically. (Liang, Correia et al. 2020) proposed an integer non-linear programming (INLP) model, to investigate the DARP of automated taxis (ATs) under dynamic travel times generated by the ATs themselves. The model's primary goal is to maximize the total daily profit of such a system by deciding on each AT's routing based on real-time information. ...
This paper explores the potential environmental benefits of ride-sharing in New York City by finding a balance between supply and demand; for the supply side considering factors such as distance and emissions while taking into account demand-side factors like waiting time and deviation from ride time (DRT). A heuristic algorithm called ADARTW (Advanced Dial-A-Ride problems with Time Windows) is used for a time-constrained version of the Dial-A-Ride problem. The algorithm creates a "pick-up window" for each request and assigns customers to vehicles by finding feasible customer insertions into the work schedules of vehicles. Furthermore, a cost function is employed to optimize the insertion process to select the best customer insertion within the algorithm. This cost function takes into consideration several key factors. Then employs a nonlinear objective function to guide the insertion process and estimate the potential reduction in the number of vehicles required for transportation. The study reveals that ride-sharing could reduce the number of vehicles by 52% and greenhouse gas emissions by 35% in NYC.
... With the increasing maturity of autonomous driving technology and its benefits in reducing transportation costs and improving the utilization of parking lots (Liang et al., 2020;Tang et al., 2021), autonomous vehicles (AVs) have been piloted to provide shuttle services in some city centers, universities, hospitals, parks, and more (RoboticResearch), as well as first/last-mile services (EasyMile). AVs do not require human drivers and are not subject to the constraint of drivers' workload (Chen et al., 2020), so using AVs to provide first-mile services may enable higher vehicle utilization and help public transit agencies in reducing operating costs. ...
The burden of first-mile connection to public transit stations is a key barrier that discourages riders from taking public transportation. Public transit agencies typically operate a modest fleet of vehicles to provide first-mile services due to the high operating costs, thus failing to adequately meet the first-mile travel demands, especially during peak hours. At the same time, private cars are underutilized and have a lot of idle time. With the emergence of self-driving vehicles, new opportunities for addressing the current dilemma arise, such as integrating idle private self-driving vehicles to provide first-mile services, which is beneficial for public transportation agencies to provide high-quality services at low costs. This study investigates the first-mile ridesharing problem in which public transit agencies utilize idle privately-owned autonomous vehicles to dynamically inflate their fleet. This problem is more challenging in decision-making than conventional first-mile problems, as it involves decisions on heterogeneous fleet scheduling, vehicle routing, and time scheduling, all while taking into account the service quality for riders. To address this problem, an arc-based mixed-integer linear programming (MILP) model and a trip-based set-partitioning model are developed, both aiming to minimize total operational costs. To identify promising trips, we propose a tailored labeling algorithm with a novel dominance rule, along with a time window shift algorithm to determine the best schedule. To yield high-quality solutions in a short computation time, a tailored column-generation matheuristic algorithm is introduced. A branch-and-price exact algorithm and an adaptive large neighborhood search algorithm are developed to assess the matheuristic algorithm. Numerical experiments are conducted to demonstrate the effectiveness and applicability of the proposed models and algorithms. Experiments also show that this kind of ridesharing service can provide low-cost and high-quality services for the first-mile problem.
... Exact algorithms were proposed (i.e., Cherkesly, Desaulniers, and Laporte 2015, Cherkesly et al. 2016, Ghilas et al. 2018, Sun, Yu, and Wang 2019, and Gschwind et al. (2018) developed a bidirectional labeling algorithm for the PDP. On the other hand, the DARP adds the ride time constraint to restrict the time between the end of service at the pickup vertex and the start of service at the delivery vertex (see Bongiovanni, Kaspi, and Geroliminis 2019, Liang et al. 2020, Paquay, Crama, and Pironet 2020, Malheiros et al. 2021, Tafreshian et al. 2021. Because of its complexity in time-related constraints, Gschwind and Drexl (2019) proposed a constant-time method to check the feasibility of a given route. ...
Motivated by the worldwide development of shared mobility, we investigate a vehicle routing problem with time windows and deadlines called the first-mile ridesharing problem (FMRSP). The FMRSP involves routing a fleet of vehicles, each servicing customers within specific time windows. The FMRSP generalizes the well-known vehicle routing problem with time windows (VRPTW), additionally imposing that each vehicle route arrives at the destination before the earliest deadline associated with the set of customers served by the route. The FMRSP is also related to the VRPTW and release dates, where in addition to time window constraints, a release date is associated with each customer defining the earliest time that the order is available to leave the depot for delivery. For the FMRSP, we present an exact method based on a branch-price-and-cut (BPC) algorithm combining state-of-the-art techniques and an innovative pricing algorithm. The pricing algorithm is based on a bidirectional bucket graph-based labeling algorithm, in which the backward extension of a label is computed in a constant time. Effective dominance rules used to speed up the computation are also described. Extensive computational studies demonstrate that our proposed BPC algorithm can solve optimality-modified Solomon benchmark instances involving up to 100 customers and real-world instances involving up to 290 customers.
Funding: This research was supported by the National Natural Science Foundation of China [Grants 71831003, 72171043, 71831006, and 71901180].
Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0139 .
... In the work (Liang et al., 2020), the authors provide an integer non-linear programming model to optimize profit for automated taxi systems. The proposed model was hard to solve since it considers different constraints and decision variables such as traffic congestion, travel times, and dynamic travel demand. ...
Ride-share platforms are contemporary businesses that match passengers with drivers, unlike taxis that can be hailed from the street. In the literature, the problem of optimizing the operations of such companies is mostly considered in static settings. We use in this paper a dynamic model and propose differential equations to model the evolution of the system. The objective is to maximize the profit during the planning horizon. Using optimal control theory, we determine the optimal rate of change in the ride price rate. An illustrative example along with sensitivity analyses shows the effect of the system parameters on the optimal solution obtained.
... Many introducing researchers focus on developing ride-sharing algorithms to make them more practical; as an example, (Alonso-Mora, Samaranayake et al. 2017) presented a mathematical model for real-time high-capacity ridesharing that scales to large numbers of travelers and journeys and generates optimal routes based on online demand and vehicle location dynamically. (Liang, Correia et al. 2020) proposed an integer non-linear programming (INLP) model, to investigate the DARP of automated taxis (ATs) under dynamic travel times generated by the ATs themselves. The model's primary goal is to maximize the total daily profit of such a system by deciding on each AT's routing based on real-time information. ...
This paper explores the potential environmental benefits of ride-sharing in New York City by finding a balance between supply and demand; for the supply side considering factors such as distance and emissions while taking into account demand-side factors like waiting time and deviation from ride time (DRT). A heuristic algorithm called ADARTW (Advanced Dial-A-Ride problems with Time Windows) is used for a time-constrained version of the Dial-A-Ride problem. The algorithm creates a "pick-up window" for each request and assigns customers to vehicles by finding feasible customer insertions into the work schedules of vehicles. Furthermore, a cost function is employed to optimize the insertion process to select the best customer insertion within the algorithm. This cost function takes into consideration several key factors. Then employs a nonlinear objective function to guide the insertion process and estimate the potential reduction in the number of vehicles required for transportation. The study reveals that ride-sharing could reduce the number of vehicles by 52% and greenhouse gas emissions by 35% in NYC.
... Based on the utilized methodology, the relevant literature can be classified into two categories. In the first category, mathematical programming-based methods are established to solve the vehicle dispatching problem, such as linear programming [8,9] and dynamic programming [10][11][12][13]. However, mathematical programmingbased methods usually assume that the traffic system is static or predictable, which is not always the case, making them less effective in dynamic and uncertain situations. ...
With the progressive technological advancement of autonomous vehicles, taxi service providers are expected to offer driverless taxi systems that alleviate traffic congestion and pollution. However, it is challenging to maintain the efficiency and reliability of a taxi service system due to the complexity of the traffic network and fluctuating traffic demand. In this paper, we present a robust variant of the twin delayed deep deterministic policy gradient algorithm (TD3), namely, adaptive TD3 integrated with robust optimization (ATD3-RO), to implement a fleet of autonomous vehicles for a taxi service under uncertain passenger demand. Our proposed method incorporates an adaptive module for integer-valued action generation, which also enhances the model’s resilience to a larger action space. Considering the uncertain demand of passengers, we design a perturbation sampling-based method to generate adversarial examples for robust training. Additionally, we propose a robust optimization-based strategy to generate a lower bound and guide the convergence of the critic network during the model training process. In our case study, we validate the efficacy of ATD3-RO by constructing a reinforcement learning simulator of the driverless taxi transportation system using real taxi data. The simulation results demonstrate that ATD3-RO outperforms the general TD3 algorithm and other state-of-the-art reinforcement-learning-based approaches while improving learning efficiency. We assess the algorithm’s robustness against sudden changes in requests, e.g., a surge in demand at some traffic nodes caused by an emergent event. The results suggest that ATD3-RO performs adaptive actions that are aligned with the variations in passenger demand. Finally, we prove that our model can provide a reliable dispatching strategy even at various ratios between driverless taxis and passenger demand.
... More recently, Gschwind & Drexl (2019) implemented an adaptive LNS combined with a set covering approach, while Malheiros et al. (2021) utilized a combination of iterative local search with a set partitioning approach for addressing heterogeneous DARP. In the context of shared autonomous vehicles with flow-dependent travel times, Liang et al. (2020) applied a customized Lagrangian relaxation algorithm within a rolling horizon framework to solve DARP for shared autonomous vehicles. Recent studies (Dong et al., 2022;Azadeh et al., 2022) incorporated users' preferences in the DARP framework via logit models, where user requests accept/reject decisions are made using profit maximization as the optimization objective. ...
Dial-a-ride (DAR) is a shared-ride service that provides mobility to transportation-disadvantaged individuals who are unable to use public transit. While most DAR studies focus on optimizing operations, our research explores the feasibility and benefits of outsourcing outlier trips to transportation network companies (TNCs) to minimize the combined service delivery cost. To achieve this goal, we formulate a multi-vehicle DAR problem (DARP) with trip outsourcing to TNCs, which can be solved optimally for small scale instances. To solve larger instances, we propose a two-stage solution framework to improve DAR routes from commercially available software. Firstly, we develop an integer programming model to re-optimize individual routes with trip outsourcing. Secondly, we design a multi-vehicle heuristic that considers reinserting trips initially designated for outsourcing back into the DAR fleet, as well as reinsertion and exchange of remaining trips among routes. We apply the approach to a medium-sized DAR operator in Maryland and achieve cost reductions of 7%-13% depending on the TNC volume discount negotiated by the DAR company.
... Such vehicle could gather data regarding traffic and optimise their routes based on the current state of congestion, as well as predict the future level of congestion, taking into consideration the route that will be travelled by other automated vehicles, and implementing adaptations to their route if needed. The wide implementation of shared automated vehicles provided that the necessary technological developments will be available, can foster this vision (see Liang et al., 2020). ...
The growing number and complexity of modern megalopolises, where several millions of inhabitants request efficient transport services, pose colossal challenges to urban mobility. To capture the ever-increasing demand for mobility without further deteriorating the traffic congestion, it is essential that the resources available are used as efficiently as possible. Besides, the traditional means of transport (train, metro, bus, taxi), each responds to a particular segment of the global demand for mobility. Nowadays, transport planners can take advantage of the progress in information technologies and optimisation methods to design modern services that integrate and coordinate different means of transport. These services are potentially capable of capturing additional segments of mobility demand and, as an outcome, reduce the usage of private vehicles. Building upon these general ideas, a growing number of researchers have studied various forms of transport flexibility as well as the integration among different means of transport. This survey provides an overview of the trends emerging from contributions from the operational research literature on urban passenger transportation. We have analysed the literature according to the dimensions of flexibility and integration of the transport service studied. For each of the application areas identified, we convey the main trends studied, summarise the most relevant solution approaches and outline some open research directions that deserve particular attention.
Partially automated systems are expected to reduce road crashes related to human error, even amongst professional drivers. Consequently, the applications of these systems into the taxi industry would potentially improve transportation safety. However, taxi drivers are prone to experiencing driving anger, which may subsequently affect their takeover performance. In this research, we explored how driving anger emotion affects taxi drivers’ driving performance in various takeover scenarios, namely Mandatory Automation-Initiated transition (MAIT), Mandatory Driver-Initiated transition (MDIT), and Optional Driver-Initiated transition (ODIT). Forty-seven taxi drivers participated in this 2·3 mixed design simulator experiment (between-subjects: anger vs. calmness; within-subjects: MAIT vs. MDIT vs. ODIT). Compared to calmness, driving anger emotion led to a narrower field of attention (e.g., smaller standard deviations of horizontal fixation points position) and worse hazard perception (e.g., longer saccade latency, smaller amplitude of skin conductance responses), which resulted in longer takeover time and inferior vehicle control stability (e.g., higher standard deviations of lateral position) in MAIT and MDIT scenarios. Angry taxi drivers were more likely to deactivate vehicle automation and take over the vehicle in a more aggressive manner (e.g., higher maximal resulting acceleration, refusing to yield to other road users) in ODIT scenarios. The findings will contribute to addressing the safety concerns related to driving anger among professional taxi drivers and promote the widespread acceptance and integration of partially automated systems within the taxi industry.
Public transportation is inconvenient in many rural areas, and the number of its users is small. However, for people who cannot drive, such as the elderly, public transportation is necessary. Therefore, public transportation needs to be maintained even if the demand is low. Although various alternatives are available, the taxi is the most suitable form of public transportation for low-demand areas. However, because of a decline in passengers and a shortage of drivers, the taxi business environment is challenging, and its sustainability is in danger. One effective solution for driver shortage is to introduce shared taxis, but its necessity decreases as the number of passengers continues to decline. Based on these factors, taxi operators have to select the mode of transportation between shared and traditional taxis. If there is a method to evaluate shared taxis compared with traditional taxis, it will be useful in mode selection. In this study, we propose a method for evaluating shared taxis based on the perspective that the lower the minimum number of drivers needed for operation, the better the sustainability of taxi operators. Specifically, using mixed integer programming, we formulate a mathematical programming model that can compute the minimum number of drivers needed, and construct an approach to evaluate shared taxis using statistical models estimated from the results calculated by the mathematical programming model. Then, we demonstrate the effectiveness of this approach through a case study.
The offline pickup and delivery problem with time windows (PDPTW) is a classical combinatorial optimization problem in the transportation community, which has proven to be very challenging computationally. Due to the complexity of the problem, practical problem instances can be solved only via heuristics, which trade-off solution quality for computational tractability. Among the various heuristics, a common strategy is problem decomposition, that is, the reduction of a large-scale problem into a collection of smaller sub-problems, with spatial and temporal decompositions being two natural approaches. While spatial decomposition has been successful in certain settings, effective temporal decomposition has been challenging due to the difficulty of stitching together the sub-problem solutions across the decomposition boundaries. In this work, we introduce a novel temporal decomposition scheme for solving a class of PDPTWs that have narrow time windows, for which it is able to provide both fast and high-quality solutions. We utilize techniques that have been popularized recently in the context of online dial-a-ride problems along with the general idea of rolling horizon optimization. To the best of our knowledge, this is the first attempt to solve offline PDPTWs using such an approach. To show the performance and scalability of our framework, we use the optimization of paratransit services as a motivating example. Due to the lack of benchmark solvers similar to ours (i.e., temporal decomposition with an online solver), we compare our results with an offline heuristic algorithm using Google OR-Tools. In smaller problem instances (with an average of 129 requests per instance), the baseline approach is as competitive as our framework. However, in larger problem instances (approximately 2,500 requests per instance), our framework is more scalable and can provide good solutions to problem instances of varying degrees of difficulty, while the baseline algorithm often fails to find a feasible solution within comparable compute times.
The offline pickup and delivery problem with time windows (PDPTW) is a classical combinatorial optimization problem in the transportation community, which has proven to be very challenging computationally. Due to the complexity of the problem, practical problem instances can be solved only via heuristics, which trade-off solution quality for computational tractability. Among the various heuristics, a common strategy is problem decomposition, that is, the reduction of a large-scale problem into a collection of smaller sub-problems, with spatial and temporal decompositions being two natural approaches. While spatial decomposition has been successful in certain settings, effective temporal decomposition has been challenging due to the difficulty of stitching together the sub-problem solutions across the decomposition boundaries. In this work, we introduce a novel temporal decomposition scheme for solving a class of PDPTWs that have narrow time windows, for which it is able to provide both fast and high-quality solutions. We utilize techniques that have been popularized recently in the context of online dial-a-ride problems along with the general idea of rolling horizon optimization. To the best of our knowledge, this is the first attempt to solve offline PDPTWs using such an approach. To show the performance and scalability of our framework, we use the optimization of paratransit services as a motivating example. We compare our results with an offline heuristic algorithm using Google OR-Tools. In smaller problem instances, the baseline approach is as competitive as our framework. However, in larger problem instances, our framework is more scalable and can provide good solutions to problem instances of varying degrees of difficulty, while the baseline algorithm often fails to find a feasible solution within comparable compute times.
This study presents an integrated optimisation framework for locating depots in a Shared autonomous vehicle (SAV) system under demand uncertainty. A two-stage stochastic mixed integer programming (MIP) model is formulated to optimise the number and locations of depots in a SAV system, where demand uncertainty is represented by multiple scenarios with occurrence probability. The dynamics of vehicle movements are further considered by forming a trip chain for each AV. An enhanced Benders decomposition-based algorithm with multiple Pareto-optimal cuts via multiple solutions is developed to solve the proposed model. The proposed modelling framework and the solution algorithm are tested using two different sizes of transportation networks. Computational analysis demonstrates that the proposed algorithm can handle large instances within acceptable computational cost, and be more efficient than the MIP solver. Meanwhile, insights regarding the optimal deployment of depots in SAV systems are also delivered under different parametric and demand pattern settings.
Autonomous vehicles (AVs) represent potentially disruptive and innovative changes to public transportation (PT) systems. However, the exact interplay between AV and PT is understudied in existing research. This paper proposes a systematic approach to the design, simulation, and evaluation of integrated autonomous vehicle and public transportation (AV + PT) systems. Two features distinguish this research from the state of the art in the literature: the first is the transit-oriented AV operation with the purpose of supporting existing PT modes; the second is the explicit modeling of the interaction between demand and supply. We highlight the transit-orientation by identifying the synergistic opportunities between AV and PT, which makes AVs more acceptable to all the stakeholders and respects the social-purpose considerations such as maintaining service availability and ensuring equity. Specifically, AV is designed to serve first-mile connections to rail stations and provide efficient shared mobility in low-density suburban areas. The interaction between demand and supply is modeled using a set of system dynamics equations and solved as a fixed-point problem through an iterative simulation procedure. We develop an agent-based simulation platform of service and a discrete choice model of demand as two subproblems. Using a feedback loop between supply and demand, we capture the interaction between the decisions of the service operator and those of the travelers and model the choices of both parties. Considering uncertainties in demand prediction and stochasticity in simulation, we also evaluate the robustness of our fixed-point solution and demonstrate the convergence of the proposed method empirically. We test our approach in a major European city, simulating scenarios with various fleet sizes, vehicle capacities, fare schemes, and hailing strategies such as in-advance requests. Scenarios are evaluated from the perspectives of passengers, AV operators, PT operators, and urban mobility system. Results show the trade off between the level of service and the operational cost, providing insight for fleet sizing to reach the optimal balance. Our simulated experiments show that encouraging ride-sharing, allowing in-advance requests, and combining fare with transit help enable service integration and encourage sustainable travel. Both the transit-oriented AV operation and the demand-supply interaction are essential components for defining and assessing the roles of the AV technology in our future transportation systems, especially those with ample and robust transit networks.
This paper presents a novel simulation model that shows the dynamic and complex nature of the innovation system of vehicle automation in a quantitative way. The model simulates the innovation diffusion of automated vehicles (AVs) on the long-term. It looks at the system of AVs from a functional perspective and therefore categorizes this technology into six different levels. Each level is represented by its own fleet size, its own technology maturity and its own average purchase price and utility. These components form the core of the model. The feedback loops between the components form a dynamic behavior that influences the diffusion of AVs. The model was applied to the Netherlands both for a base and an optimistic scenario (strong political support and technology development) named "AV in-bloom". In these experiments, we found that the system is highly uncertain with market penetration varying greatly with the scenarios and policies adopted. Having an 'AV in bloom' ecosystem for AVs is connected with a great acceleration of the market take-up of high levels of automation. As a policy instrument, a focus on more knowledge transfer and the creation of an external fund (e.g. private investment funds or European research funds) has shown to be most effective to realize a positive innovation diffusion for AVs. Providing subsidies may be less effective as these give a short-term impulse to a higher market penetration, but will not be able to create a higher market surplus for vehicle automation.
This paper examines the changes that might result from the large-scale uptake of a shared and self-driving fleet of vehicles in a mid-sized European city. The work explores two different self-driving vehicle concepts – a ridesharing system (Shared Taxi), which emulates a taxi-like system where customers accept small detours from their original direct path and share part of their ride with others and a dynamic bus-like service with minibuses (Taxi-Bus), where customers pre-book their service at least 30 minutes in advance (permanent bookings for regular trips should represent most requests) and walk short distances to a designated stop. Under the premise that the “upgraded” system should as much as possible deliver the same trips as today in terms of origin, destination and timing, and that it should also replace all car and bus trips, it looks at impacts on car fleet size, volume of travel and parking requirements. Mobility output and CO2 emissions are also detailed in two different time scales (24 hr. average and peak-hour only). The obtained results suggest that a full implementation scenario where the existing metro service is kept and private car, bus and taxi mobility would be replaced by shared modes would significantly reduce travelled vehicle.kilometres and CO2 emissions.
Data used in the paper:
Correia, G.H., van Arem, B., 2016. Solving the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP): A model to explore the impacts of self-driving vehicles on urban mobility. Transportation Research Part B: Methodological 87, 64–88.
Part of the data is the OVIN mobility data collected in the Netherlands:
https://www.cbs.nl/nl-nl/onze-diensten/methoden/onderzoeksomschrijvingen/korte-onderzoeksbeschrijvingen/onderzoek-verplaatsingen-in-nederland--ovin--
Shared autonomous (fully-automated) vehicles (SAVs) represent an emerging transportation mode for driverless and on-demand transport. Early actors include Google and Europe’s CityMobil2, who seek pilot deployments in low-speed settings. This work investigates SAVs’ potential for U.S. urban areas via multiple applications across the Austin, Texas, network. This work describes advances to existing agent- and network-based SAV simulations by enabling dynamic ride-sharing (DRS, which pools multiple travelers with similar origins, destinations and departure times in the same vehicle), optimizing fleet sizing, and anticipating profitability for operators in settings with no speed limitations on the vehicles and at adoption levels below 10 % of all personal trip-making in the region. Results suggest that DRS reduces average service times (wait times plus in-vehicle travel times) and travel costs for SAV users, even after accounting for extra passenger pick-ups, drop-offs and non-direct routings. While the base-case scenario (serving 56,324 person-trips per day, on average) suggest that a fleet of SAVs allowing for DRS may result in vehicle-miles traveled (VMT) that exceed person-trip miles demanded (due to anticipatory relocations of empty vehicles, between trip calls), it is possible to reduce overall VMT as trip-making intensity (SAV membership) rises and/or DRS users become more flexible in their trip timing and routing. Indeed, DRS appears critical to avoiding new congestion problems, since VMT may increase by over 8 % without any ride-sharing. Finally, these simulation results suggest that a private fleet operator paying 1.00 per mile for a non-shared trip (which is less than a third of Austin’s average taxi cab fare).
A number of technological advances have led to a renewed interest in dynamic vehicle routing problems. This survey classifies routing problems from the perspective of information quality and evolution. After presenting a general description of dynamic routing, we introduce the notion of degree of dynamism, and present a comprehensive review of applications and solution methods for dynamic vehicle routing problems.
From: A Short History of Carsharing in the 90's. Journal of World Transport Policy & Practice 5 (3), 18 - 40 The vast majority of automobile trips in U.S. metropolitan regions are drive-alone car trips. In 1990, approximately 90 percent of work trips and 58 percent of nonwork trips in the United States were made by vehicles with only one occupant (United States Department of Transportation, 1995). Vehicles are unused an average of 23 hours per day. This form of transportation is expensive and consumes large amounts of land. Private vehicles are attractive. Their universal appeal is demonstrated by rapid motorization rates, even in countries with high fuel prices, good transit systems, and relatively compact land development. But the environmental, resource, and social costs of widespread car use are also high. One strategy for retaining the benefits of car use while limiting costs is to create institutions for sharing vehicles. The principle of carsharing is simple: Individuals gain the benefits of private cars without the costs and responsibilities of ownership. Instead of owning one or more vehicles, a household accesses a fleet of vehicles on an as-needed basis. Carsharing may be thought of as organized short-term car rental. Individuals gain access to vehicles by joining organizations that maintain a fleet of cars and light trucks in a network of vehicle locations. Generally, participants pay a usage fee each time they use a vehicle.
Smartphone technology enables dynamic ride-sharing systems that bring together people with similar itineraries and time schedules to share rides on short-notice. This paper considers the problem of matching drivers and riders in this dynamic setting. We develop optimization-based approaches that aim at minimizing the total system-wide vehicle miles incurred by system users, and their individual travel costs. To assess the merits of our methods we present a simulation study based on 2008 travel demand data from metropolitan Atlanta. The simulation results indicate that the use of sophisticated optimization methods instead of simple greedy matching rules substantially improve the performance of ride-sharing systems. Furthermore, even with relatively low participation rates, it appears that sustainable populations of dynamic ride-sharing participants may be possible even in relatively sprawling urban areas with many employment centers.
Abstract With greater availability of real-time information systems, algorithms are needed to support commercial,eet operators in their decisions to assign ve- hicles and drivers to loads in a dynamic environment. We present a rolling horizon framework for the dynamic,assignment and sequencing of trucks to jobs consisting of picking up and delivering full truckloads when requests for Submitted for presentation at the 78th Annual Meeting of the Transportation Research Board, January 1999, Washington, D.C. and publication in Transportation Research Board. 1 service arise on a continuous basis. A mathematical formulation of the problem faced at each stage is presented; its solution allows for the dynamic reassign- ment of trucks to loads, including diversion to a new load of a truck already en-route to pick up another load, as well as for the dynamic resequencing of the order in which loads are to be served, as new loads arrive and conditions unfold. Loads have associated time windows for pick up and delivery, and the objective function includes explicit penalty cost for not serving a particular load. A so- lution algorithm is presented and implemented, and computational results are presented, yielding insight into various operational trade-os in dynamic eet operations. Because applicability of the solution algorithm is at present limited to relatively small problems, and given the stochastic dynamic nature of these systems, numerical experiments are performed to compare the quality of the solution obtained using this approach to the performance of simpler and less
An inventory routing problem in crude oil transportation is studied, in which crude oil is transported from a supply center to multiple customer harbors to satisfy their demands over multiple periods. In the problem, a heterogeneous fleet of tankers consisting of tankers owned by a distributor and tankers rented from a third party, a pipeline, and multiple types of routes are considered; both inventory level and shortage level at each customer harbor are limited. The objective is to determine for each period over a given time horizon the number of tankers of each type to be rented/returned at the supply center, the number of tankers of each type to be dispatched on each route, and the quantity of crude oil flowing through the pipeline that minimizes the total logistics cost.After formulating the problem as a mixed integer programming problem, a Lagrangian relaxation approach is developed for finding a near optimal solution of the problem. The approach is also applied to a variant of the problem in which both fully and partially loaded tankers are allowed in the transportation of crude oil. Numerical experiments show that this approach outperforms an existing meta-heuristic algorithm, especially for the instances of large sizes.
The asymmetric demand-offer problem represents a major challenge for one-way vehicle sharing systems (VSS) affecting their economic viability as it necessitates the engagement of considerable human (and financial) resources in relocating vehicles to satisfy customer demand. In this paper, we propose a novel approach which involves user-based vehicle relocations to address supply-and-demand mismatches; in our approach, VSS users are offered price incentives so as to accept picking up their vehicle from an oversupplied station and/or to drop it off to an under-supplied station. The system incentivizes users based on the priorities of vehicle relocations among stations, taking into account the fluctuating demand for vehicles and parking places at different stations over time. A graph-theoretic approach is employed for modeling the problem of allocating vehicles to users in a way that maximizes the profit of the system taking into account the budget the VSS can afford to spend for rewarding users, as well as the users’ strategic behavior. We present two different schemes for incentivizing users to act in favour of the system. Both schemes consider budget constraints and are truthful and budget-feasible. We have extensively evaluated our approach through simulations which demonstrated significant gain with respect to the number of completed trips and system revenue. We have also validated our approach through pilot trials conducted in a free-floating e-motorbike sharing system in the framework of an EU-funded research project.
A considerable amount of research has been devoted to understanding and modeling carsharing services in the past. However, a research question that has not been investigated yet is how carsharing companies affect each other in a competitive market. This paper presents a methodology, based on the MATSim framework, for investigating the interaction of carsharing competitors and uses it to analyze the possible competition of two free-floating carsharing companies in the city of Zurich, Switzerland. It further provides a first attempt to allow agents to make mode-choice decisions based on a discrete choice model that includes carsharing alternatives on a whole plan level. Relocation capabilities are added to the carsharing module that allow for testing of various relocation algorithms.
The paper investigates the competition of carsharing operators on two levels. First, by investigating the impact of different price levels, and second, by allowing one or both of the competitors to perform relocations. The obtained results show that (1) with fixed market rate, operators can obtain the highest profit by offering the service at 0.41 CHF/min, (2) offering different pricing schemes can lead to a higher cumulative payoff, although one of the competitors is left with a reduced profit and (3) performing relocations is unprofitable for the company tasked with the burden of doing it, whilst a competitor may enjoy a free-rider effect with an increasing number of rentals and higher profit.
Motivated by the growth of ridesourcing services and the expected advent of fully-autonomous vehicles (AVs), this paper defines, models, and compares assignment strategies for a shared-use AV mobility service (SAMS). Specifically, the paper presents the on-demand SAMS with no shared rides, defined as a fleet of AVs, controlled by a central operator, that provides direct origin-to-destination service to travelers who request rides via a mobile application and expect to be picked up within a few minutes. The underlying operational problem associated with the on-demand SAMS with no shared rides is a sequential (i.e. dynamic or time-dependent) stochastic control problem. The AV fleet operator must assign AVs to open traveler requests in real-time as traveler requests enter the system dynamically and stochastically. As there is likely no optimal policy for this sequential stochastic control problem, this paper presents and compares six AV-traveler assignment strategies (i.e. control policies). An agent-based simulation tool is employed to model the dynamic system of AVs, travelers, and the intelligent SAMS fleet operator, as well as, to compare assignment strategies across various scenarios. The results show that optimization-based AV-traveler assignment strategies, strategies that allow en-route pickup AVs to be diverted to new traveler requests, and strategies that incorporate en-route drop-off AVs in the assignment problem, reduce fleet miles and decrease traveler wait times. The more-sophisticated AV-traveler assignment strategies significantly improve operational efficiency when fleet utilization is high (e.g. during the morning or evening peak); conversely, when fleet utilization is low, simply assigning traveler requests sequentially to the nearest idle AV is comparable to more-advanced strategies. Simulation results also indicate that the spatial distribution of traveler requests significantly impacts the empty fleet miles generated by the on-demand SAMS.
One-way station-based carsharing systems allow users to return a rented car to any designated station, which could be different from the origin station. Existing research has been mainly focused on the vehicle relocation problem to deal with the travel demand fluctuation over time and demand imbalance in space. However, the strategic planning of the stations’ location and their capacity for one-way carsharing systems has not been well studied yet, especially when considering vehicle relocations simultaneously. This paper presents a Mixed-integer Non-linear Programming (MINLP) model to solve the carsharing station location and capacity problem with vehicle relocations. This entails considering several important components which are for the first time integrated in the same model. Firstly, relocation operations and corresponding relocation costs are taken into consideration to address the imbalance between trip requests and vehicle availability. Secondly, the flexible travel demand at various time steps is taken as the input to the model avoiding deterministic requests. Thirdly, a logit model is constructed to represent the non-linear demand rate by using the ratio of carsharing utility and private car utility. To solve the MINLP model, a customized gradient algorithm is proposed. The application to the SIP network in Suzhou, China, demonstrates that the algorithm can solve a real world large scale problem in reasonable time. The results identify the pricing and parking space rental costs as the key factors influencing the profitability of carsharing operators. Also, the carsharing station location and fleet size impact the vehicle relocation and carsharing patronage.
The one-commodity pickup and delivery problem (1-PDP) has a wide range of applications in the real world, e.g., for repositioning bikes in large cities to guarantee the sustainable operations of bike-sharing systems. It remains a challenge, however, to solve the problem for large-scale instances. This paper proposes a hybrid modeling framework for 1-PDP, where a continuum approximation (CA) approach is used to model internal pickup and delivery routing within each of multiple subregions, while matching of net surplus or deficit of the commodity out of these subregions is addressed in a discrete model with a reduced problem size. The interdependent local routing and system-level matching decisions are made simultaneously, and a Lagrangian relaxation based algorithm is developed to solve the hybrid model. A series of numerical experiments are conducted to show that the hybrid model is able to produce a good solution for large-scale instances in a short computation time.
There has been a resurgence of interest in demand-responsive shared-ride systems, motivated by concerns for the environment and also new developments in technologies which enable new modes of operations. This paper surveys the research developments on the Dial-A-Ride Problem (DARP) since 2007. We provide a classification of the problem variants and the solution methodologies, and references to benchmark instances. We also present some application areas for the DARP, discuss some future trends and challenges, and indicate some possible directions for future research.
Object positioning and surveillance has been playing an important role in various indoor location-aware applications. Signal attenuation or blockage often requires multiple local sensors to be used jointly to provide coverage and determine object locations via mobile devices. The deployment of sensors has a significant impact on the accuracy of positioning and effectiveness of surveillance. In this paper, we develop a reliable sensor location model that aims at optimizing the location of sensors so as to maximize the accuracy of object positioning/surveillance under the risk of possible sensor disruptions. We formulate the problem as a mixed-integer linear program and develop solution approaches based on a customized Lagrangian relaxation algorithm with an embedded approximation subroutine. A series of hypothetical examples and a real-world Wi-Fi access point design problem for Chicago O'Hare Airport Terminal 5 are used to demonstrate the applicability of the model and solution algorithms. Managerial insights are also presented.
This paper proposes a method of assigning trips to automated taxis (ATs) and designing the routes of those vehicles in an urban road network while considering the traffic congestion caused by this dynamic responsive service. The system is envisioned to provide a seamless door-to-door service within a city area for all passengers’ origins and destinations. An integer programming model is proposed to define the routing of the vehicles according to a profit maximization function while depending on dynamic travel times which vary with the ATs’ flow. This will be especially important when the number of automated vehicles (AVs) circulating on the roads is high enough that their routing will cause delays. This system should be able to serve not only the reserved travel requests but also some real-time requests. A rolling horizon scheme is used to divide one day into several periods in which both the real-time and the booked demand will be considered together. The model is applied to the real size case study city of Delft, the Netherlands. The results allow assessing the impact of the ATs movements on traffic congestion and the profitability of the system. From the case-study, it is possible to conclude that taking into account the effect of the vehicle flows on travel time leads to changes in the system profit, the satisfied percentage and the driving distance of the vehicles, which points out the importance of this type of model in the assessment of the operational effects of ATs in the future.
Autonomous vehicle (AV) technology holds great promise for improving the efficiency of traditional vehicle sharing systems. In this paper, we investigate a new vehicle sharing system using AVs, referred to as autonomous vehicle sharing and reservation (AVSR). In such a system, travelers can request AV trips ahead of time and the AVSR system operator will optimally arrange AV pickup and delivery schedules and AV trip chains based on these requests. A linear programming model is proposed to efficiently solve for optimal solutions for AV trip chains and required fleet size through constructed AVSR networks. Case studies show that AVSR can significantly increase vehicle use rate (VUR) and consequentially reduce vehicle ownership significantly. In the meantime, it is found that the actual vehicle miles traveled (VMT) in AVSR systems is not significantly more than that of conventional taxis, despite inevitable empty hauls for vehicle relocation in AVSR systems. The results imply huge potential benefits from AVSR systems on improving mobility and sustainability of our current transportation systems.
We study the shared autonomous vehicle (SAV) routing problem while considering congestion. SAVs essentially provide a dial-a-ride service to travelers, but the large number of vehicles involved (tens of thousands of SAVs to replace personal vehicles) results in SAV routing causing significant congestion. We combine the dial-a-ride service constraints with the linear program for system optimal dynamic traffic assignment, resulting in a congestion-aware formulation of the SAV routing problem. Traffic flow is modeled through the link transmission model, an approximate solution to the kinematic wave theory of traffic flow. SAVs interact with travelers at origins and destinations. Due to the large number of vehicles involved, we use a continuous approximation of flow to formulate a linear program. Optimal solutions demonstrate that peak hour demand is likely to have greater waiting and in-vehicle travel times than off-peak demand due to congestion. SAV travel times were only slightly greater than system optimal personal vehicle route choice. In addition, solutions can determine the optimal fleet size to minimize congestion or maximize service.
In the recent years many developments took place regarding automated vehicles (AVs) technology. It is however unknown to which extent the share of the existing transport modes will change as result of AVs introduction as another public transport option. This study is the first where detailed traveller preferences for AVs are explored and compared to existing modes. Its main objective is to position AVs in the transportation market and understand the sensitivity of travellers towards some of their attributes, focusing particularly on the use of these vehicles as egress mode of train trips. Because fully-automated vehicles are not yet a reality and they entail a potentially high disruptive way on how we use automobiles today, we apply a stated preference experiment where the role of attitudes in perceiving the utility of AVs is particularly explored in addition to the classical instrumental variables and several socio-economic variables. The estimated discrete choice model shows that first class train travellers on average prefer the use of AVs as egress mode, compared to the use of bicycle or bus/tram/metro as egress. We therefore conclude that AVs as last mile transport between the train station and the final destination have most potential for first class train travellers. Results show that in-vehicle time in AVs is experienced more negatively than in-vehicle time in manually driven cars. This suggests that travellers do not perceive the theoretical advantage of being able to perform other tasks during the trip in an automated vehicle, at least not yet. Results also show that travellers’ attitudes regarding trust and sustainability of AVs are playing an important role in AVs attractiveness, which leads to uncertainty on how people will react when AVs are introduced in practice. We therefore state the importance of paying sufficient attention to these psychological factors, next to classic instrumental attributes like travel time and costs, before and during the implementation process of AVs as a public transport alternative. We recommend the extension of this research to revealed preference studies, thereby using the results of field studies.
Shared autonomous vehicles (SAVs) could provide inexpensive mobility on-demand services. In addition, the autonomous vehicle technology could facilitate the implementation of dynamic ride-sharing (DRS). The widespread adoption of SAVs could provide benefits to society, but also entail risks. For the design of effective policies aiming to realize the advantages of SAVs, a better understanding of how SAVs may be adopted is necessary. This article intends to advance future research about the travel behavior impacts of SAVs, by identifying the characteristics of users who are likely to adopt SAV services and by eliciting willingness to pay measures for service attributes. For this purpose, a stated choice survey was conducted and analyzed, using a mixed logit model. The results show that service attributes including travel cost, travel time and waiting time may be critical determinants of the use of SAVs and the acceptance of DRS. Differences in willingness to pay for service attributes indicate that SAVs with DRS and SAVs without DRS are perceived as two distinct mobility options. The results imply that the adoption of SAVs may differ across cohorts, whereby young individuals and individuals with multimodal travel patterns may be more likely to adopt SAVs. The methodological limitations of the study are also acknowledged. Despite a potential hypothetical bias, the results capture the directionality and relative importance of the attributes of interest.
Interest in vehicle automation has been growing in recent years, especially with the very visible Google car project. Although full automation is not yet a reality there has been significant research on the impacts of self-driving vehicles on traffic flows, mainly on interurban roads. However, little attention has been given to what could happen to urban mobility when all vehicles are automated. In this paper we propose a new method to study how replacing privately owned conventional vehicles with automated ones affects traffic delays and parking demand in a city. The model solves what we designate as the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP), which dynamically assigns family trips in their automated vehicles in an urban road network from a user equilibrium perspective where, in equilibrium, households with similar trips should have similar transport costs. Automation allows a vehicle to travel without passengers to satisfy multiple household trips and, if needed, to park itself in any of the network nodes to benefit from lower parking charges. Nonetheless, the empty trips can also represent added congestion in the network. The model was applied to a case study based on the city of Delft, the Netherlands. Several experiments were done, comparing scenarios where parking policies and value of travel time (VTT) are changed. The model shows good equilibrium convergence with a small difference between the general costs of traveling for similar families. We were able to conclude that vehicle automation reduces generalized transport costs, satisfies more trips by car and is associated with increased traffic congestion because empty vehicles have to be relocated. It is possible for a city to charge for all street parking and create free central parking lots that will keep total transport costs the same, or reduce them. However, this will add to congestion as traffic competes to access those central nodes. In a scenario where a lower VTT is experienced by the travelers, because of the added comfort of vehicle automation, the car mode share increases. Nevertheless this may help to reduce traffic congestion because some vehicles will reroute to satisfy trips which previously were not cost efficient to be done by car. Placing the free parking in the outskirts is less attractive due to the extra kilometers but with a lower VTT the same private vehicle demand would be attended with the advantage of freeing space in the city center.
We consider two stochastic variants of the Share-a-Ride problem: one with stochastic travel times and one with stochastic delivery locations. Both variants are formulated as a two-stage stochastic programming model with recourse. The objective is to maximize the expected profit of serving a set of passengers and parcels using a set of homogeneous vehicles. Our solution methodology integrates an adaptive large neighborhood search heuristic and three sampling strategies for the scenario generation (fixed sample size sampling, sample average approximation, and sequential sampling procedure). A computational study is carried out to compare the proposed approaches. The results show that the convergence rate depends on the source of stochasticity in the problem: stochastic delivery locations converge faster than stochastic travel times according to the numerical test. The sample average approximation and the sequential sampling procedure show a similar performance. The performance of the fixed sample size sampling is better compared to the other two approaches. The results suggest that the stochastic information is valuable in real-life and can dramatically improve the performance of a taxi sharing system, compared to deterministic solutions.
The objective of this work is to provide analytical guidelines and financial justification for the design of shared-vehicle mobility-on-demand systems. Specifically, we consider the fundamental issue of determining the appropriate number of vehicles to field in the fleet, and estimate the financial benefits of several models of car sharing. As a case study, we consider replacing all modes of personal transportation in a city such as Singapore with a fleet of shared automated vehicles, able to drive themselves, e.g., to move to a customer’s location. Using actual transportation data, our analysis suggests a shared-vehicle mobility solution can meet the personal mobility needs of the entire population with a fleet whose size is approximately 1/3 of the total number of passenger vehicles currently in operation.
Automation may be assumed to have a beneficial impact on traffic flow efficiency. However, the relationship between automation and traffic flow efficiency is complex because behavior of road users influences this efficiency as well. This paper reviews what is known about the influence of automation on traffic flow efficiency and behavior of road users, formulates a theoretical framework, and identifies future research needs. It is concluded that automation can be assumed to have an influence on traffic flow efficiency and on the behavior of road users. The research has shortcomings, and in this context directions are formulated for future scientific research on automation in relation to traffic flow efficiency and human behavior.
This paper presents and compares online implementations of rejected-reinsertion heuristics for the dynamic multivehicle dial-a-ride problem (DARP), which the authors previously developed for static DARP. In dynamic DARP, transportation requests are received in real time, whereas in static DARP, all information about service requests is known in advance. The main objective for the DARP heuristics is to minimize the number of vehicles used to satisfy all trip requests, subject to service quality constraints. Two online implementation strategies, called "immediate insertion" and "rolling-horizon insertion," coupled with two variations of the insertion heuristic rejected reinsertion without and with periodic improvement procedures, are developed and compared. Computational results show that the rolling-horizon insertion heuristics, which take advantage of the advance information available, achieve vehicle reductions of up to 10% more than their immediate-insertion counterpart. The proposed online rejected-reinsertion heuristics achieve vehicle reductions up to 16% and 10% more, respectively, than the online parallel insertion heuristics that use immediate insertion and rolling-horizon insertion strategies, while keeping the computation time at the same magnitude as the parallel-insertion heuristics. Sensitivity analysis shows that the rolling-horizon insertion heuristics are insensitive to the time horizon and the rolling interval.
Carsharing programs that operate as short-term vehicle rentals (often for one-way trips before ending the rental) like Car2Go and ZipCar have quickly expanded, with the number of US users doubling every 1–2 years over the past decade. Such programs seek to shift personal transportation choices from an owned asset to a service used on demand. The advent of autonomous or fully self-driving vehicles will address many current carsharing barriers, including users’ travel to access available vehicles.This work describes the design of an agent-based model for shared autonomous vehicle (SAV) operations, the results of many case-study applications using this model, and the estimated environmental benefits of such settings, versus conventional vehicle ownership and use. The model operates by generating trips throughout a grid-based urban area, with each trip assigned an origin, destination and departure time, to mimic realistic travel profiles. A preliminary model run estimates the SAV fleet size required to reasonably service all trips, also using a variety of vehicle relocation strategies that seek to minimize future traveler wait times. Next, the model is run over one-hundred days, with driverless vehicles ferrying travelers from one destination to the next. During each 5-min interval, some unused SAVs relocate, attempting to shorten wait times for next-period travelers.Case studies vary trip generation rates, trip distribution patterns, network congestion levels, service area size, vehicle relocation strategies, and fleet size. Preliminary results indicate that each SAV can replace around eleven conventional vehicles, but adds up to 10% more travel distance than comparable non-SAV trips, resulting in overall beneficial emissions impacts, once fleet-efficiency changes and embodied versus in-use emissions are assessed.
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.
Dynamic ride-share systems aim to bring together travelers with similar itineraries and time schedules on short-notice. These systems 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 dynamic ride-share system. We systematically outline the optimization challenges that arise when developing technology to support ride-sharing and survey the related operations research models in the academic literature. We hope that this paper will encourage more research by the transportation science and logistics community in this exciting, emerging area of public transportation.
A tool to assess the market potential for new carsharing operations in urban communities is examined and applied. The research is based on the analysis conducted for TCRP Report 108: Carsharing: Where and How It Succeeds. Geographic market segments in urban areas are analyzed. A geographic information system (GIS)–based analysis of 13 U.S. regions finds that neighborhood and transportation characteristics are more important indicators for carsharing success than the individual demographics of carsharing members. Results indicate that low vehicle ownership has the strongest, most consistent correlation to the amount of carsharing service in a neighborhood. Thresholds based on analysis results are outlined for low service (i.e., carsharing may be viable but limited growth can be expected) and high service (i.e., carsharing is likely to flourish). This tool to identify neighborhoods that can support carsharing is applied to a community seeking to establish a carsharing program: Austin, Texas. The analysis finds that several Austin neighborhoods have the characteristics to support carsharing (e.g., low vehicle ownership rates and high percentages of one-person households), but few Austin neighborhoods could support a high level of carsharing service.
An investigation of the single-vehicle, many-to-many, immediate-request dial-a-ride problem is developed in two parts. Part I focuses on the ″static″ case of the problem. A generalized objective function is examined, the minimization of a weighted combination of the time to service all customers and of the total degree of ″dissatisfaction″ experienced by them while waiting for service. A dynamic programming approach is developed. The algorithm exhibits a computational effort. Part II extends this approach to solving the equivalent ″dynamic″ case. Example in both ″static″ and ″dynamic″ cases are presented.
Members of carsharing organizations reduce both the number of vehicles owned and vehicle miles traveled (VMT). Given these benefits at the individual level, carsharing may interest policy makers as another tool to address the negative environmental, economic, and social consequences of automobile dependence. However, the aggregate effects of carsharing must be estimated before sound policy decisions can be made. This paper describes a Monte Carlo simulation of the economic decision to own or share a vehicle on the basis of major cost components and past vehicle use. The simulation estimates the percentage of vehicles that would be cheaper to share than own. In Baltimore, Maryland, this result ranged from 4.2% under a traditional neighborhood carsharing model to 14.8% in a commuter-based carsharing model. Sensitivity analyses identified travel time and VMT as the most important economic factors, which likely incorporate other factors such as transit access and environmental attitudes. Because travel behavior, not ownership cost, drives the economic carsharing decision, the model hypothesizes that there will be increasing marginal societal benefits from policies that promote carsharing. The model can be applied to any geographic area and can be used to assess carsharing impacts of various policies that change the economics of owning or driving an auto. These results indicate that carsharing can become prevalent enough to be considered an important policy tool.
The increase of urban traffic congestion calls for studying alternative measures for mobility management, and one of these measures is carpooling. In theory, these systems could lead to great reductions in the use of private vehicles; however, in practice they have obtained limited success for two main reasons: the psychological barriers associated with riding with strangers and poor schedule flexibility. To overcome some of the limitations of the traditional schemes, we proposed studying a carpooling club model with two main new features: establishing a base trust level for carpoolers to find compatible matches for traditional groups and at the same time allowing to search for a ride in an alternative group when the pool member has a trip schedule different from the usual one. A web-based survey was developed for the Lisbon Metropolitan Region (Portugal), including a Stated Preference experiment, to test the concept and confirm previous knowledge on these systems' determinants. It was found through a binary logit Discrete Choice Model calibration that carpooling is still attached with lower income strata and that saving money is still an important reason for participating in it. The club itself does not show promise introducing more flexibility in these systems; however, it should provide a way for persons to interact and trust each other at least to the level of working colleagues.
The authors propose a new model for dynamic traffic assignment,
modeling the traffic system by a mixed integer linear program solvable
in finite time. The model represents link travel times, which must be
the same for all vehicles which enter a link together during a single
time period by means of 0-1 integer variables. Given the values of these
variables, the problem is to assign traffic, modeled as multiperiod
multicommodity flow, subject to constraints on capacity implied by the
link travel times. An optimal solution to the model gives the vehicle
routings corresponding to minimum total travel time, achieving the most
efficient use of road capacity. The solution gives unambiguous link
travel times as a function of time of entry to the link, suitable for
individual route optimization if all but a small priority class of
traffic accepts the system-optimal routing
We address a problem of vehicle routing that arises in picking up and delivering full container load from/to an intermodal terminal. The substantial cost and time savings are expected by efficient linkage between pickup and delivery tasks, if the time of tasks and the suitability of containers for cargo allow. As this problem is NP-hard, we develop a subgradient heuristic based on a Lagrangian relaxation which enables us to identify a near optimal solution. The heuristic consists of two sub-problems: the classical assignment problem and the generalized assignment problem. As generalized assignment problem is also NP-hard, we employ an efficient solution procedure for a bin packing based problem, which replaces the generalized assignment problem. The heuristic procedure is tested on a wide variety of problem examples. The test results demonstrate that the procedure developed here can efficiently solve large instances of the problem.
This research focuses on planning biofuel refinery locations where the total system cost for refinery investment, feedstock and product transportation and public travel is minimized. Shipment routing of both feedstock and product in the biofuel supply chain and the resulting traffic congestion impact are incorporated into the model to decide optimal locations of biofuel refineries. A Lagrangian relaxation based heuristic algorithm is introduced to obtain near-optimum feasible solutions efficiently. To further improve optimality, a branch-and-bound framework (with linear programming relaxation and Lagrangian relaxation bounding procedures) is developed. Numerical experiments with several testing examples demonstrate that the proposed algorithms solve the problem effectively. An empirical Illinois case study and a series of sensitivity analyses are conducted to show the effects of highway congestion on refinery location design and total system costs.
Abstract The Dial-a-Ride Problem (DARP) consists of designing vehicle routes and schedules for n users who specify pickup and delivery requests between origins and destinations. The aim is to plan a set of m minimum,cost vehicle routes capable of accommodating,as many users as possible, under a set of constraints. The most common example arises in door-to-door transportation for elderly or disabled people. The purpose of this article is to review the scientific literature on the DARP. The main features of the problem are described and a summary,of the most important models and algorithms is provided. Key Words: Dial-a-ride problem – survey – static and dynamic pickup and delivery
The Vehicle Routing Problem (VRP) was introduced 50 years ago by Dantzig and Ramser under the title “The Truck Dispatching Problem.” The study of the VRP has given rise to major developments in the fields of exact algorithms and heuristics. In particular, highly sophisticated exact mathematical programming decomposition algorithms and powerful metaheuristics for the VRP have been put forward in recent years. The purpose of this article is to provide a brief account of this development.
(This article originally appeared in Management Science, January 1981, Volume 27, Number 1, pp. 1–18, published by The Institute of Management Sciences.)
One of the most computationally useful ideas of the 1970s is the observation that many hard integer programming problems can be viewed as easy problems complicated by a relatively small set of side constraints. Dualizing the side constraints produces a Lagrangian problem that is easy to solve and whose optimal value is a lower bound (for minimization problems) on the optimal value of the original problem. The Lagrangian problem can thus be used in place of a linear programming relaxation to provide bounds in a branch and bound algorithm. This approach has led to dramatically improved algorithms for a number of important problems in the areas of routing, location, scheduling, assignment and set covering. This paper is a review of Lagrangian relaxation based on what has been learned in the last decade.
Self-driving cars: the next revolution
- Kpmg
KPMG, 2012. Self-driving cars: the next revolution.
- X Liang
X. Liang, et al.
Transportation Research Part C 112 (2020) 260-281