Empty Vehicle Redistribution Research Articles

Empty Vehicle Redistribution with Time Windows in Autonomous Taxi Systems

In this article, we investigate empty vehicle redistribution algorithms with time windows for personal rapid transit or autonomous station-based taxi services, from a passenger service perspective. We present an Index Based Redistribution Time Limited algorithm that improves upon existing algorithms by incorporating expected passenger arrivals and predicted waiting times limitations. We evaluate 17 variations of algorithms on a test case in Stockholm, Sweden. The results show that the combination of Send The Nearest and Index Based Redistribution Time Limited algorithms provides promising results for both Poisson arrivals and real demand, outperforming the other tested methods, in terms of passenger waiting time and number of passengers not served within their time windows.

Empty vehicle redistribution in autonomous taxi services

In this article, we investigate empty vehicle redistribution algorithms for Personal Rapid Transit (PRT) or autonomous station-based taxi services, from a passenger service perspective. We present a new index-based redistribution (IBR) algorithm that improves upon existing nearest neighbour and indexing algorithms by incorporating expected passenger arrivals and predicted waiting times into the surplus/deficit index. We evaluate six variations of algorithms on a test case in Paris Saclay, France. The results show that especially the combination of Simple Nearest Neighbours + Index Based Redistribution provides promising results for both off-peak and rush-hour demand, outperforming the other methods tested, in terms of passenger waiting time (average and maximum) as well as station queue lengths.

The matching problem of empty vehicle redistribution in autonomous taxi systems

This article discusses empty vehicle redistribution algorithms for PRT and autonomous taxi services from a passenger service perspective. In modern literature reactive methods such as nearest neighbours are commonly used. In this article we first formulate the general matching problem on a bipartite graph of available vehicles and stations. In addition, we propose an index-based proactive redistribution (IBR) 18,19 algorithm based on predicted near-future demand at stations. The results of different redistribution methods implemented on a simple line test case show that none of the proposed methods are optimal in all cases. Test results of six variations of combined proactive and reactive strategies on a test case in Paris Saclay, France with 20 stations and 100 vehicles are given. The combined Nearest Neighbour / IBR provides a promising solution for both peak and off-peak demand, significantly outperforming all other methods considered, in terms of passenger waiting time (both average and maximum) as well as in terms of station queue lengths.

Empty vehicle redistribution and fleet size in autonomous taxi systems

This study investigates empty vehicle redistribution algorithms for personal rapid transit and autonomous taxi services. The focus is on passenger service and operator cost. A new redistribution algorithm is presented in this study: index-based redistribution (IBR). IBR is a proactive method, meaning it takes into account both current demand and anticipated future demand, in contrast to reactive methods, which act based on current demand only. From information on currently waiting for passengers, predicted near-future demand and projected arrival of vehicles, IBR calculates an index for each vehicle station, and redistribution is done based on this index. Seven different algorithm combinations are evaluated using a test case in Paris Saclay, France (20 stations and 100 vehicles). A combination of simple nearest neighbours and IBR is shown to be promising. Its results outperform the other methods tested in peak and off-peak demand, in terms of average and maximum passenger waiting times as well as station queue length. The effect of vehicle fleet size on generalised cost is analysed. Waiting times, mileage and fleet size are taken into account while assessing this generalised cost.

The matching problem of empty vehicle redistribution in autonomous taxi systems

This article discusses empty vehicle redistribution algorithms for PRT and autonomous taxi services from a passenger service perspective. In modern literature reactive methods such as nearest neighbours are commonly used. In this article we first formulate the general matching problem on a bipartite graph of available vehicles and stations. In addition, we propose a new index-based proactive redistribution (IBR) algorithm based on predicted near-future demand at stations. Test results of six variations of combined proactive and reactive strategies on a test case in Saclay, France with 20 stations and 100 vehicles are given. The combined Nearest Neighbour / IBR provides a promising solution for both peak and off-peak demand, significantly outperforming all other methods considered, in terms of passenger waiting time (both average and maximum) as well as in terms of station queue lengths.

Reducing the wasted transportation capacity of Personal Rapid Transit systems: An integrated model and multi-objective optimization approach

This study addresses the problem of wasted transportation capacity in Personal Rapid Transit (PRT) systems. We propose a two-tier transportation model that integrates PRT and capillary transportation systems. We study a related multi-objective empty vehicle redistribution problem that attempts to minimize empty movement and the number of vehicles used. Furthermore, we design a hybrid multi-objective genetic algorithm that integrates multiple crossover operators and linear programming techniques to solve the proposed problem. Evaluations indicate that our algorithm produces satisfactory results, and simulations confirm the efficiency of our proposed two-tier transportation system.

Proactive empty vehicle redistribution for personal rapid transit and taxis

The empty vehicle redistribution (EVR) problem is to decide when and where to move empty vehicles in a Personal Rapid Transit or taxi system. These decisions are made in real time by an EVR algorithm. A reactive EVR algorithm moves empty vehicles only in response to known requests; in contrast, a proactive EVR algorithm moves empty vehicles in anticipation of future requests. This paper describes two new proactive EVR algorithms, called sampling and voting (SV) and dynamic transportation problem (DTP), that move empty vehicles proactively based on demand estimates from historical data. It also develops methods for assessing the performance of EVR algorithms absolutely in terms of both throughput and passenger waiting times. In simulation tests, the proposed algorithms provide lower passenger waiting times than other algorithms in the literature, and proactive movement of empty vehicles significantly reduces waiting times, usually with a modest increase in empty vehicle travel.

Sampling of Redistribution of Empty Vehicles for Personal Rapid Transit

A personal rapid transit (PRT) system uses compact, computer-guided vehicles running on dedicated guideways to carry individuals or small groups directly between pairs of stations. PRT vehicles operate on demand when a passenger requests service at his or her origin station. Because the number of trips requested from a station need not equal the number of trips ending there, some empty vehicles must move to balance the flows. The empty vehicle redistribution (EVR) problem prompts the decision to move empty vehicles reactively in response to known requests or proactively in anticipation of requests. This paper develops a new algorithm for the EVR problem called “sampling and voting” (SV). SV results in the choice of reactive movements with the use of a simple nearest-neighbor rule; the selection of proactive movements is derived by the generation of an ensemble of possible sequences of future passenger requests, the solution of a deterministic optimization problem for each sequence individually, and the discovery of the empty vehicle movements that are common among the sequences. Moving vehicles proactively is essential for providing short passenger waiting times. The new SV algorithm was tested in simulation with several case study systems, and it produced significantly shorter passenger waiting times than did existing EVR algorithms. Variants of the SV method developed here for PRT are also applicable to conventional taxi systems and emergency response systems.

Theoretical Maximum Capacity as Benchmark for Empty Vehicle Redistribution in Personal Rapid Transit

A personal rapid transit system uses compact, computer-guided vehicles running on dedicated guideways to carry individuals or small groups directly between pairs of stations. Vehicles move on demand when a passenger requests service at his or her origin station. Because the number of trips requested from a station need not equal the number of trips ending there, some vehicles must run empty to balance the flows. The empty vehicle redistribution (EVR) problem is to decide which empty vehicles to move and when and where to move them; an EVR algorithm makes these decisions in real time, as passengers arrive and request service. A method was developed for finding the theoretical maximum demand (with a given spatial distribution) that a given system could serve with any EVR algorithm, which provides a benchmark against which particular EVR algorithms can be compared. The maximum passenger demand that a particular EVR algorithm can serve can be determined by simulation and then compared with the benchmark. The method is applied to two simple EVR heuristics on two example systems. The results suggest that this is a useful method for determining the strengths and weaknesses of a variety of EVR heuristics across a range of networks, passenger demands, and fleet sizes.

Optimal empty vehicle redistribution for hub‐and‐spoke transportation systems

AbstractThis article considers the empty vehicle redistribution problem in a hub‐and‐spoke transportation system, with random demands and stochastic transportation times. An event‐driven model is formulated, which yields the implicit optimal control policy. Based on the analytical results for two‐depot systems, a dynamic decomposition procedure is presented which produces a near‐optimal policy with linear computational complexity in terms of the number of spokes. The resulting policy has the same asymptotic behavior as that of the optimal policy. It is found that the threshold‐type control policy is not usually optimal in such systems. The results are illustrated through small‐scale numerical examples. Through simulation the robustness of the dynamic decomposition policy is tested using a variety of scenarios: more spokes, more vehicles, different combinations of distribution types for the empty vehicle travel times and loaded vehicle arrivals. This shows that the dynamic decomposition policy is significantly better than a heuristics policy in all scenarios and appears to be robust to the assumptions of the distribution types. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008

Optimal threshold control of empty vehicle redistribution in two depot service systems

In this note, we consider the empty vehicle redistribution problem in a two-depot service system with random demands and uncertain transportation times. It is shown that the optimal stationary policy is of threshold control-type when the long-run average cost is to be minimized. The explicit form of the average cost under threshold controls is presented, which can be used to calculate the optimal threshold values. The sufficient and necessary conditions for the existence of the steady-state are provided. The results are then used to construct suboptimal policies for a more realistic model.

Research opportunities in transportation system characteristics and operations

This paper describes several major research opportunities in the area of transportation “supply” over the next 3–5 years. The general focus of all these topics is on service design. These research topics are classified into three principal categories: capacity provision, vehicle scheduling and vehicle control. In the category of capacity provision, the major research topics are airside airport capacity, freight-terminal location and vehicle-fleet sizing. Vehicle scheduling topics include route and schedule design, empty-vehicle redistribution and routing of hazardous materials. The topics discussed in the category of vehicle control are highway safety, air-traffic control and traffic-management systems. Associated with these various topical areas are five important methodological themes: (a) vehicle utilization; (b) development of stochastic models; (c) decentralized control of complex systems; (d) data collection, processing and presentation; (e) supply-demand interactions.