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1.
Ride-hailing is a clear initial market for autonomous electric vehicles (AEVs) because it features high vehicle utilization levels and strong incentive to cut down labor costs. An extensive and reliable network of recharging infrastructure is the prerequisite to launch a lucrative AEV ride-hailing fleet. Hence, it is necessary to estimate the charging infrastructure demands for an AEV fleet in advance. This study proposes a charging system planning framework for a shared-use AEV fleet providing ride-hailing services in urban area. We first adopt an agent-based simulation model, called BEAM, to describe the complex behaviors of both passengers and transportation systems in urban cities. BEAM simulates the driving, parking and charging behaviors of the AEV fleet with range constraints and identifies times and locations of their charging demands. Then, based on BEAM simulation outputs, we adopt a hybrid algorithm to site and size charging stations to satisfy the charging demands subject to quality of service requirements. Based on the proposed framework, we estimate the charging infrastructure demands and calculate the corresponding economics and carbon emission impacts of electrifying a ride-hailing AEV fleet in the San Francisco Bay Area. We also investigate the impacts of various AEV and charging system parameters, e.g., fleet size, vehicle battery capacity and rated power of chargers, on the ride-hailing system’s overall costs. 相似文献
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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. 相似文献
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This paper addresses a Time Dependent Capacitated Vehicle Routing Problem with stochastic vehicle speeds and environmental concerns. The problem has been formulated as a Markovian Decision Process. As distinct from the traditional attempts on the problem, while estimating the amount of fuel consumption and emissions, the model takes time-dependency and stochasticity of the vehicle speeds into account. The Time Dependent Capacitated Vehicle Routing Problem is known to be NP-Hard for even deterministic settings. Incorporating uncertainty to the problem increases complexity, which renders classical optimization methods infeasible. Therefore, we propose an Approximate Dynamic Programming based heuristic as a decision aid tool for the problem. The proposed Markovian Decision Model and Approximate Dynamic Programming based heuristic are flexible in terms that more environmentally friendly solutions can be obtained by changing the objective function from cost minimization to emissions minimization. The added values of the proposed decision support tools have been shown through computational analyses on several instances. The computational analyses show that incorporating vehicle speed stochasticity into decision support models has potential to improve the performance of resulting routes in terms of travel duration, emissions and travel cost. In addition, the proposed heuristic provides promising results within relatively short computation times. 相似文献
6.
Routing and scheduling software is part of the information and technology systems available to support the transport industry, and uses complex algorithms along with geographical representations of the road network to allow better planning of daily collection and delivery schedules. This paper reviews the evolution of routing and scheduling software, the algorithms used along with reported barriers to wider take-up and potential industry-driven improvements that could be made. A survey of transport companies in the U.K. was conducted in order to validate and prioritise the software capabilities that require the most development according to the new challenges that the industry is facing. Responses suggested that companies required improved route optimisation to tackle congestion based on time-dependent data and models, and greater accuracy in the representation of the road network. Not considering congestion leads to the underestimation of travel times and the production of inaccurate schedules. Literature shows that operational research techniques are available to solve problems that represent real-world conditions, but research into the relative merits of using time-dependent models needs to be undertaken. Data might be improved by cooperation between government and private sector. 相似文献
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The number of vehicles on the road (worldwide) is constantly increasing, causing traffic jams and congestion especially in city traffic. Anticipatory vehicle routing techniques have thus far been applied to fairly small networked traffic scenarios and uniform traffic. We note here a number of limitations of these techniques and present a routing strategy on the assumption of a city map that has a large number of nodes and connectivity and where the vehicles possess highly varying speed capabilities. A scenario of operation with such characteristics has not previously been sufficiently studied in the literature. Frequent short‐term planning is preferred as compared with infrequent planning of the complete map. Experimental results show an efficiency boost when single‐lane overtaking is allowed, traffic signals are accounted for and every vehicle prefers to avoid high traffic density on a road by taking an alternative route. Comparisons with optimistic routing, pessimistic routing and time message channel routing are given. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
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Autonomous vehicles admit consideration of novel traffic behaviors such as reservation-based intersection controls and dynamic lane reversal. We present a cell transmission model formulation for dynamic lane reversal. For deterministic demand, we formulate the dynamic lane reversal control problem for a single link as an integer program and derive theoretical results. In reality, demand is not known perfectly at arbitrary times in the future. To address stochastic demand, we present a Markov decision process formulation. Due to the large state size, the Markov decision process is intractable. However, based on theoretical results from the integer program, we derive an effective heuristic. We demonstrate significant improvements over a fixed lane configuration both on a single bottleneck link with varying demands, and on the downtown Austin network. 相似文献
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Seung Hyun Kim 《运输规划与技术》2016,39(6):574-585
The delivery service provided by large-scale retailers continues to grow as online sales occupy an increasingly large share of the market. This study aims to tease out efficient vehicle scheduling times as well as optimal delivery routes by applying meta-heuristic algorithms. Monthly data on existing routes were obtained from a branch of Korea’s leading large-scale online retailer. The first task was to examine the status of existing routes by comparing delivery routes created using Dijkstra’s algorithm with existing delivery routes and their vehicle scheduling. The second task was to identify optimal delivery routes through a comparative analysis of the genetic algorithm and Tabu search algorithm, known for its superior applicability amongst other meta-heuristic algorithms. These findings demonstrate that the optimal vehicle routing problem not only has the potential to reduce distribution costs for operators and expedite delivery for consumers, but also the added social benefit of reduced carbon emissions. 相似文献
10.
Traffic pollution is an increasing challenge for cities. Emissions such as nitrogen dioxides pose a major health threat to the city’s inhabitants. These emissions often accumulate to critical levels in local areas of the city. To react to these critical emission levels, cities start implementing dynamic traffic management systems (TMS). These systems dynamically redirect traffic flows away from critical areas. These measures impact the travel speeds within the city. This is of particular importance for parcel delivery companies. These companies deliver goods to customers in the city. To avoid long delivery times and higher costs, companies already adapt their routing with respect to changing traffic conditions. Still, a communication with the TMS may allow anticipatory planning to avoid potentially critical areas in the city. In this paper, we show how communication between TMS and delivery companies results in benefits for both parties. To exploit the provided information, we develop a dynamic routing policy anticipating potential future measures of the TMS. We analyze our algorithm in a comprehensive case study for the TMS of the city of Braunschweig, Germany, a city often used as reference for a typical European city layout. We show that for the delivery company, integrating the TMS’ information in their routing algorithms reduces the driving times significantly. For the TMS, providing the information results in less traffic in the polluted areas. 相似文献
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Reservation-based intersection control is a revolutionary idea for using connected autonomous vehicle technologies to improve intersection controls. Vehicles individually request permission to follow precise paths through the intersection at specific times from an intersection manager agent. Previous studies have shown that reservations can reduce delays beyond optimized signals in many demand scenarios. The purpose of this paper is to demonstrate that signals can outperform reservations through theoretical and realistic examples. We present two examples that exploit the reservation protocol to prioritize vehicles on local roads over vehicles on arterials, increasing the total vehicle delay. A third theoretical example demonstrates that reservations can encourage selfish route choice leading to arbitrarily large queues. Next, we present two realistic networks taken from metropolitan planning organization data in which reservations perform worse than signals. We conclude with significantly positive results from comparing reservations and signals on the downtown Austin grid network using dynamic traffic assignment. Overall, these results indicate that network-based analyses are needed to detect adverse route choices before traffic signals can be replaced with reservation controls. In asymmetric intersections (e.g. local road-arterial intersections), reservation controls can cause several potential issues. However, in networks with more symmetric intersections such as a downtown grid, reservations have great potential to improve traffic. 相似文献
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In this paper, we study the impact of using a new intelligent vehicle technology on the performance and total cost of a European port, in comparison with existing vehicle systems like trucks. Intelligent autonomous vehicles (IAVs) are a new type of automated guided vehicles (AGVs) with better maneuverability and a special ability to pick up/drop off containers by themselves. To identify the most economical fleet size for each type of vehicle to satisfy the port’s performance target, and also to compare their impact on the performance/cost of container terminals, we developed a discrete-event simulation model to simulate all port activities in micro-level (low-level) details. We also developed a cost model to investigate the present values of using two types of vehicle, given the identified fleet size. Results of using the different types of vehicles are then compared based on the given performance measures such as the quay crane net moves per hour and average total discharging/loading time at berth. Besides successfully identifying the optimal fleet size for each type of vehicle, simulation results reveal two findings: first, even when not utilising their ability to pick up/drop off containers, the IAVs still have similar efficacy to regular trucks thanks to their better maneuverability. Second, enabling IAVs’ ability to pick up/drop off containers significantly improves the port performance. Given the best configuration and fleet size as identified by the simulation, we use the developed cost model to estimate the total cost needed for each type of vehicle to meet the performance target. Finally, we study the performance of the case study port with advanced real-time vehicle dispatching/scheduling and container placement strategies. This study reveals that the case study port can greatly benefit from upgrading its current vehicle dispatching/scheduling strategy to a more advanced one. 相似文献
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The vehicle routing problem (VRP) is a critical and vital problem in logistics for the design of an effective and efficient transportation network, within which the capacitated vehicle routing problem (CVRP) has been widely studied for several decades due to the practical relevance of logistics operation. However, CVRP with the objectives of minimizing the overall traveling distance or the traveling time cannot meet the latest requirements of green logistics, which concern more about the influence on the environment. This paper studies CVRP from an environmental perspective and introduces a new model called environmental vehicle routing problem (EVRP) with the aim of reducing the adverse effect on the environment caused by the routing of vehicles. In this research, the environmental influence is measured through the amount of the emission carbon dioxide, which is a widely acknowledged criteria and accounts for the major influence on environment. A hybrid artificial bee colony algorithm (ABC) is designed to solve the EVRP model, and the performance of the hybrid algorithm is evaluated through comparing with well-known CVRP instances. The computational results from numerical experiments suggest that the hybrid ABC algorithm outperforms the original ABC algorithm by 5% on average. The transformation from CVRP to EVRP can be recognized through the differentiation of their corresponding optimal solutions, which provides practical insights for operation management in green logistics. 相似文献
14.
Can agents measure up? A comparative study of an agent-based and on-line optimization approach for a drayage problem with uncertainty 总被引：1，自引：0，他引：1
Tams Mhr Jordan Srour Mathijs de Weerdt Rob Zuidwijk 《Transportation Research Part C: Emerging Technologies》2010,18(1):99-119
Experiments studying the behavior of agent-based methods over varying levels of uncertainty in comparison to traditional optimization methods are generally absent from the literature. In this paper we apply two structurally distinct solution approaches, an on-line optimization and an agent-based approach, to a drayage problem with time windows under two types of uncertainty. Both solution approaches are able to respond to dynamic events. The on-line optimization approach utilizes a mixed integer program to obtain a feasible route at 30-s intervals. The second solution approach deploys agents that engage in auctions to satisfy their own objectives based on the information they perceive and maintain locally. Our results reveal that the agent-based system can outperform the on-line optimization when service time duration is highly uncertain. The on-line optimization approach, on the other hand, performs competitively with the agent-based system under conditions of job-arrival uncertainty. When both moderate service time and job-arrival uncertainties are combined, the agent system outperforms the on-line optimization; however, in the case of extremely high combined uncertainty, the on-line optimization outperforms the agent-based approach. 相似文献
15.
This paper provides a globally optimal solution to an important problem: given a real-world route, what is the most energy-efficient way to drive a vehicle from the origin to the destination within a certain period of time. Along the route, there may be multiple stop signs, traffic lights, turns and curved segments, roads with different grades and speed limits, and even leading vehicles with pre-known speed profiles. Most of such route information and features are actually constraints to the optimal vehicle speed control problem, but these constraints are described in two different domains. The most important concept in solving this problem is to convert the distance-domain route constraints to some time-domain state and input constraints that can be handled by optimization methods such as dynamic programming (DP). Multiple techniques including cost-to-go function interpolation and parallel computing are used to reduce the computation of DP and make the problem solvable within a reasonable amount of time on a personal computer. 相似文献
16.
In driving simulation, a scenario includes definitions of the road environment, the traffic situation, simulated vehicles’ interactions with the participant’s vehicle and measurements that need to be collected. The scenarios need to be designed in such a way that the research questions to be studied can be answered, which commonly imply exposing the participant for a couple of predefined specific situations that has to be both realistic and repeatable. This article presents an integrated algorithm based on Dynamic Actor Preparation and Automated Action Planning to control autonomous simulated vehicles in the simulation in order to generate predefined situations. This algorithm is thus able to plan driving actions for autonomous vehicles based on specific tasks with relevant contextual information as well as handling longitudinal transportation of simulated vehicles based on the contextual information in an automated manner. The conducted experiment shows that the algorithm is able to guarantee repeatability under autonomous traffic flow. The presented algorithm can benefit not only the driving simulation community, but also relevant areas, such as autonomous vehicle and in-vehicle device design by providing them with an algorithm for target pursue and driving task accomplishment, which can be used to design a human-vehicle cooperation system in the coming era of autonomous driving. 相似文献
17.
This paper introduces a fleet size and mix dial-a-ride problem with multiple passenger types and a heterogeneous fleet of reconfigurable vehicles. In this new variant of the dial-a-ride problem, en-route modifications of the vehicle’s inner configuration are allowed. The main consequence is that the vehicle capacity is defined by a set of configurations and the choice of vehicle configuration is associated with binary decision variables.The problem is modeled as a mixed-integer program derived from the model of the heterogeneous dial-a-ride problem. Vehicle reconfiguration is a lever to efficiently reduce transportation costs, but the number of passengers and vehicle fleet setting make this problem intractable for exact solution methods. A large neighborhood search metaheuristic combined with a set covering component with a reactive mechanism to automatically adjust its parameters is therefore proposed. The resulting framework is evaluated against benchmarks from the literature, used for similar routing problems. It is also applied to a real case, in the context of the transportation of disabled children from their home to medical centers in the city of Lyon, France. 相似文献
18.
Javier Alonso Vicente Milanés Joshué Pérez Enrique Onieva Carlos González Teresa de Pedro 《Transportation Research Part C: Emerging Technologies》2011,19(6):1095-1110
This article presents a cooperative manoeuvre among three dual mode cars – vehicles equipped with sensors and actuators, and that can be driven either manually or autonomously. One vehicle is driven autonomously and the other two are driven manually. The main objective is to test two decision algorithms for priority conflict resolution at intersections so that a vehicle autonomously driven can take their own decision about crossing an intersection mingling with manually driven cars without the need for infrastructure modifications. To do this, the system needs the position, speeds, and turning intentions of the rest of the cars involved in the manoeuvre. This information is acquired via communications, but other methods are also viable, such as artificial vision. The idea of the experiments was to adjust the speed of the manually driven vehicles to force a situation where all three vehicles arrive at an intersection at the same time. 相似文献
19.
This paper focuses on modeling agents’ en-route diversion behavior under information provision. The behavior model is estimated based on naïve Bayes rules and re-calibrated using a Bayesian approach. Stated-preference driving simulator data is employed for model estimation. Bluetooth-based field data is employed for re-calibration. Then the behavior model is integrated with a simulation-based dynamic traffic assignment model. A traffic incident scenario along with variable message signs (VMS) is designed and analyzed under the context of a real-world large-scale transportation network to demonstrate the integrated model and the impact of drivers’ dynamic en-route diversion behavior on network performance. Macroscopic Fundamental Diagram (MFD) is employed as a measurement to represent traffic dynamics. This research has quantitatively evaluated the impact of information provision and en-route diversion in a VMS case study. It proposes and demonstrates an original, complete, behaviorally sound, and cost-effective modeling framework for potential analyses and evaluations related to Advanced Traffic Information System (ATIS) and real-time operational applications. 相似文献
20.
Autonomous vehicles have the potential to improve link and intersection traffic behavior. Computer reaction times may admit reduced following headways and increase capacity and backwards wave speed. The degree of these improvements will depend on the proportion of autonomous vehicles in the network. To model arbitrary shared road scenarios, we develop a multiclass cell transmission model that admits variations in capacity and backwards wave speed in response to class proportions within each cell. The multiclass cell transmission model is shown to be consistent with the hydrodynamic theory. This paper then develops a car following model incorporating driver reaction time to predict capacity and backwards wave speed for multiclass scenarios. For intersection modeling, we adapt the legacy early method for intelligent traffic management (Bento et al., 2013) to general simulation-based dynamic traffic assignment models. Empirical results on a city network show that intersection controls are a major bottleneck in the model, and that the legacy early method improves over traffic signals when the autonomous vehicle proportion is sufficiently high. 相似文献