Vehicle Relocation Research Articles

Optimal Transport Pricing in an Age of Fully Autonomous Vehicles: Is It Getting More Complicated?

Over the last several decades, transportation scientists have made substantial progress in identifying and tackling transport-related problems by elaborating sophisticated policy instruments. Originally, the policy instruments were developed and designed to tackle inefficiencies caused by conventional (human-driven) vehicles. However, questions remain regarding transportation policies, especially pricing instruments, in the future. With the advent of fully autonomous vehicles (driverless or self-driving cars), many of potentially disruptive changes to our transportation system are projected to occur. This gives rise to the question of how to adapt the existing, well established, policy instruments to make them applicable to a world of self-driving cars. The present paper utilizes one of the most widely deployed, most important (in terms of tax revenue), and most effective (in terms of carbon dioxide mitigation) current price-based policy instruments in the transport sector (i.e., the energy tax) to show how one of the most innovative features associated with fully autonomous vehicles (i.e., driverless vehicle relocation) affects the optimal design of a transportation tax. We adopt a microeconomics optimization framework and analytically derive the optimal energy tax under the assumption that driverless vehicle relocation is possible. Our main finding is that in a world of self-driving cars, the energy tax (likewise, a second-best miles tax) as a price-based policy instrument becomes more difficult to evaluate. With the capability of fully autonomous vehicles to relocate without passengers inside, the (analytical) expression for the optimal energy tax becomes more complex, and its (numerical) determination becomes more difficult since the feature of driverless vehicle repositioning imposes counteracting welfare effects as a response to a tax change. Policymakers and researches are encouraged to take on the challenge of increasing complexity to tackle transport-related inefficiencies in the era of self-driving cars.

Energy and greenhouse gas implications of shared automated electric vehicles

Automated vehicles can facilitate vehicle sharing within and between households, thus decreasing ownership rates, albeit increasing miles travelled. While electrification can achieve deep reductions in transportation-related greenhouse gas emissions, shared automated electric vehicles will have different charging needs considering their higher mileage. In this study, travel survey data collected in Toronto is used to optimize the charging needs of shared automated electric vehicles, with the objective of reducing greenhouse gas emissions from electricity generation by choosing charging times that are associated with lower marginal emission factors. Optimized charging schedules reduce greenhouse gas emissions by at least 50% relative to vehicles charging once they return home. Life cycle assessment of multiple sharing scenarios indicates that higher levels of sharing increase emissions. Indeed, empty mileage associated with vehicle relocation and higher deterioration lead to a higher fleet turnover. With vehicle sharing, there is a risk of increasing mileage and vehicle production emissions.

Detection of Lower Body for AGV Based on SSD Algorithm with ResNet

Detection of human lower body provides an implementation idea for the automatic tracking and accurate relocation of automatic vehicles. Based on traditional SSD and ResNet, this paper proposes an improved detection algorithm R-SSD for human lower body detection, which utilizes ResNet50 instead of VGG16 to improve the feature extraction level of the model. According to the application of acquisition equipment, the model input resolution is increased to 448 × 448 and the model detection range is expanded. Six feature maps of the updated resolution network are selected for detection and the lower body image dataset is clustered into five categories for aspect ratio, which are evenly distributed to each feature detection map. The experimental results show that the model R-SSD detection accuracy after training reaches 85.1% mAP. Compared with the original SSD, the detection accuracy is improved by 7% mAP. The detection confidence in practical application reaches more than 99%, which lays the foundation for subsequent tracking and relocation for automatic vehicles.

VEHICLE RELOCATION EFFICIENCY – A CASE STUDY OF THE PARKING COMPANY IN THE CITY OF ZAGREB

There is a need for vehicle relocation, whether for delivering new vehicles to their selling destinations, pulling inoperative or damaged vehicles out of traffiic, or removing illegally parked vehicles from the street. The relocation of illegally parked vehicles in the City of Zagreb is done by constantly monitoring parked vehicles or through citizen telephone alerts. The paper analyses the vehicle relocation service of the Zagrebparking company, which, in addition to overseeing car park management in public car parks and garages, is in charge of removing illegally parked vehicles. Due to the cyclic nature of its operations, a high share of empty rides, high vehicle investment costs, small quantities of cargo transported, high fuel prices, and high fuel consumption in the urban environment, Zagrebparking’s Vehicle Relocation Department operates with unnecessarily high costs. In this paper, vehicle relocation efficiency is analysed by presenting indicators of vehicle utilisation as part of a systematic evaluation of the company. Improvements to the current service are also provided.

Putting ridesharing to the test: efficient and scalable solutions and the power of dynamic vehicle relocation

We study the optimization of large-scale, real-time ridesharing systems and propose a modular design methodology, Component Algorithms for Ridesharing (CAR). We evaluate a diverse set of CARs (14 in total), focusing on the key algorithmic components of ridesharing. We take a multi-objective approach, evaluating 10 metrics related to global efficiency, complexity, passenger, and platform incentives, in settings designed to closely resemble reality in every aspect, focusing on vehicles of capacity two. To the best of our knowledge, this is the largest and most comprehensive evaluation to date. We (i) identify CARs that perform well on global, passenger, or platform metrics, (ii) demonstrate that lightweight relocation schemes can significantly improve the Quality of Service by up to 50%, and (iii) highlight a practical, scalable, on-device CAR that works well across all metrics.

A hybrid adaptive large neighborhood search and tabu search algorithm for the electric vehicle relocation problem

With the raising environmental worries, the sharing economy and electric vehicles gain popularity rapidly. It is crucial to relocate vehicles to make them available for customers in the car-sharing system. In this paper, we study the electric vehicle relocation problem where the triangle inequality may not hold. The objective is to determine the routing schedules for staffs to re-balance the spatial and temporal distribution of electric vehicles with the highest total profit of the relocated requests. Constraints including time windows, limited durations and charging requirements are considered. To solve this problem, we develop a hybrid adaptive large neighborhood search and tabu search algorithm, where a tailored duration computation scheme is embedded. Extensive computational experiments are conducted based on randomly generated instances. The results demonstrate the competitive performance of our algorithm both in solution quality and solving time.

One-way carsharing service design under demand uncertainty: A service reliability-based two-stage stochastic program approach

This study proposes a two-stage stochastic program approach to jointly optimize the planning and operational decisions for one-way carsharing systems accounting for demand uncertainty. The two-stage stochastic model is constructed in the way that stage-one determines the strategic deployments, including the number of vehicles, personnel and parking lots at each carsharing station, which is referred to as the regular service. Upon the realization of the random demand, stage-two solves the ad hoc service and optimizes vehicle relocation and personnel assignment at the operational level. The ad hoc service is integrated into the model, which serves the excess demand that cannot be covered by the regular service. For computational efficiency, we introduce the concept of service reliability (SR) to decompose the original two-stage stochastic formulation into separable sub-problems. A SR-based gradient solution procedure is then applied to efficiently solve the problem. The performance of the proposed model and algorithm is demonstrated with numerical experiments on a toy and a realistic network. Sensitivity analysis on demand variations and ad hoc service costs is further conducted to evaluate impacts of these key factors on the system performance. Results show that up to 20.49% improvement in the profit can be achieved by the proposed stochastic approach compared to the solution obtained from the historical average model.

Repositioning of driverless cars: Is return to home rather than downtown parking economically viable?

As of today, a considerable amount of urban land is devoted to parking, especially in downtown areas. Fully autonomous vehicles (FAVs) – also referred to as self-driving car or driverless car – can self-park in less-expensive areas or even have no need to park at all and so can free city centers from parking lots and thereby relieve downtown land for more valuable purposes such as housing, production, recreation. This paper analyzes whether the technological capability of FAVs to reposition could also be economically viable under a wide range of differentiated constellations, paying particular attention to the heterogeneity of (downtown) parking pricing schemes (hourly parking vs. daily parking vs. employer-paid parking). We develop an economic speed choice model and calculate the utility maximizing (cost minimizing) driving speed of an empty electric FAV returning home. It is shown that compared to human driven cars, individual welfare losses when deviating from the privately optimal speed are rather small, implying that congestion effects would hardly affect economic viability of ‘return home’, as opposed to feasibility. We estimate feasible ‘return home’ options of an empty FAV depending on stylized parameters such as the average duration of stay of the traveler and distance between origin (home) and destination (e.g. work). To identify economic viability, we then juxtapose the net benefit of vehicle repositioning with the cost of downtown parking. Our analysis suggests that vehicle repositioning in the form of ‘return home’ may be economically viable for a wide range of constellations, in particular when account is taken of the benefits of intra-household car-sharing made possible by vehicle relocation. Our findings highlight the potential of FAVs to revolutionize the world’s transportation system and urban structure and stress the need to carefully fine-tune transportation policies to tackle the potential adverse societal effects of induced vehicle miles traveled owing to driverless vehicle repositioning.

Branch-and-price-and-cut for the electric vehicle relocation problem in one-way carsharing systems

We study the electric vehicle (EV) relocation problem that arises in the practice of one-way carsharing systems. In this problem, users are allowed to pick up an EV from one parking station and return it to another station. To maintain a spatially balanced distribution of vehicles, the system creates a set of pickup and delivery requests of EVs, which are to be served by a team of workers. The objective of this problem is to find a set of routes for workers to fulfill EV pickup and delivery requests, such that the total operational profit is maximized. The battery level of EV and the working duration limit for workers make the problem complicated and challenging. We formulate a set-packing model for this problem, and design a branch-and-cut-and-price algorithm to solve it. Particularly, we design a bi-directional label-setting algorithm to deal with the pricing subproblem, and implement two acceleration techniques, namely, decremental space relaxation and heuristic column generator. We also consider another objective of minimizing the total route duration for this problem, and the proposed algorithm still works with some adaptations applied. On a set of randomly generated instances, we conduct extensive computational experiments, and the results demonstrate the effectiveness of the proposed algorithm. Besides, the impacts of instance parameters and the efficiency of acceleration techniques have been analyzed.

Evaluating the Impacts of Optimization Horizon on the Shared Autonomous Vehicle Reservation Request System

With the development of technology, shared autonomous vehicles may become one of the main traffic modes in the future. Especially, shared autonomous vehicle reservation system, commuting, and other trips with fixed departure time mostly submit their travel requests in advance. Therefore, it is important to reasonably match shared autonomous vehicles and reservation demands. In this paper, reservation requests are divided into short-term and long-term requests by inputting requests in a more realistic way. An integer linear programming model considering operator scheduling cost and system service level is established. A detailed scheme considering rolling horizon continuity and ridesharing is used to improve the dispatching result. Based on traffic data in Delft, the Netherlands, 164 scenarios are tested in which the parking cost, fuel cost, ridesharing effect, service level, and network size are analyzed. The results show that a better relocation and ridesharing matching scheme can be obtained when the rolling horizon is small, while the overall effect is better when the rolling horizon is large. Moreover, the buffer time, distance, and travel time limit for vehicle relocation should be selected according to the request quantity and the calculation time requirement. The result can provide a suggestion for the dispatching of shared autonomous vehicle reservation system with ridesharing.

Dynamic Search of Train Shortest Routes Within Microscopic Traffic Simulators

Computer simulations are frequently used for rail traffic optimization. This approach, referred to as simulation-based optimization, typically employs simulation tools – simulators that are designed to examine railway systems at various levels of detail. Microscopic rail traffic simulators find use when examining rail traffic and the rail infrastructure in great detail. Such simulators typically serve to follow the positions and motions of rail vehicles (trains, locomotives, train cars) and their relocation as well as segments of the rail infrastructure (tracks, switches, track crossings). One of the typical problems to be solved by microscopic simulators within a simulation experiment is to determine the realistic train and shunting routes (within the currently occupied infrastructure) along which the rail vehicles are moved. This requires a suitable rail infrastructure model, associated with algorithms that seek admissible routes for the transfer of the relocation objects. This paper describes novel dynamic route searching algorithms applicable to the relocation of rail vehicles within track infrastructure of railway systems. The infrastructure model is represented by a specific edge-weighted undirected graph. The routes computed by the algorithms coincide with specific shortest walks in the graph, taking into account the particular relocation object lengths. The use of the algorithms within the simulation tools (working at the microscopic level of detail) extends the modelling possibilities when searching for realistic track routes (especially for complicated shunting operations), which contributes to better modelling of complex railway traffic (than in the relevant existing rail traffic simulators) and thus to better application of the results of traffic simulations in practice.

Real-Time Vehicle Relocation and Staff Rebalancing Problem for Electric and Shared Vehicle Systems

Real-Time Vehicle Relocation and Staff Rebalancing Problem for Electric and Shared Vehicle Systems

Real-time Vehicle Relocation and Charging Optimization for One-way Electric Carsharing Systems

Real-time Vehicle Relocation and Charging Optimization for One-way Electric Carsharing Systems

Collaborative strategic and tactical planning for one-way station-based carsharing systems with trip selection and vehicle relocation

ABSTRACT Carsharing emerges as a flexible and sustainable transportation mode to deal with severe urban challenges. This paper addresses a joint design problem of carsharing systems by an integer linear programming (ILP) model. Strategic planning of stations’ number, location and parking capacities and tactical planning of the fleet size and initial vehicle distribution are collaboratively optimized by considering all-day vehicle relocation and dynamic trip selection at the operational level. For the first time, vehicle relocation and demand selection are integrated with the joint optimization of strategic and tactical planning. We present a greedy heuristic algorithm embedding ILP to solve the problem. The case study of Chengdu demonstrates that the algorithm is computationally efficient when dealing with large-scale problems. The results identify how vehicle relocation and trip selection work in different scenes of travel time and demands and we conclude operators should target at medium-long trips by discount or promotion.

Multi-Depot Electric Bus Scheduling Considering Operational Constraint and Partial Charging: A Case Study in Shenzhen, China

Electric buses (e-buses) demonstrate great potential in improving urban air quality thanks to zero tailpipe emissions and thus being increasingly introduced to the public transportation systems. In the transit operation planning, a common requirement is that long-distance non-service travel of the buses among bus terminals should be avoided in the schedule as it is not cost-effective. In addition, e-buses should begin and end a day of operation at their base depots. Based on the unique route configurations in Shenzhen, the above two requirements add further constraint to the form of feasible schedules and make the e-bus scheduling problem more difficult. We call these two requirements the vehicle relocation constraint. This paper addresses a multi-depot e-bus scheduling problem considering the vehicle relocation constraint and partial charging. A mixed integer programming model is formulated with the aim to minimize the operational cost. A Large Neighborhood Search (LNS) heuristic is devised with novel destroy-and-repair operators to tackle the vehicle relocation constraint. Numerical experiments are conducted based on multi-route operation cases in Shenzhen to verify the model and effectiveness of the LNS heuristic. A few insights are derived on the decision of battery capacity, charging rate and deployment of the charging infrastructure.

Idle vehicle relocation strategy through deep learning for shared autonomous electric vehicle system optimization

In optimization of a shared autonomous electric vehicle (SAEV) system, idle vehicle relocation strategies are important to reduce operation costs and customers’ wait time. However, for an on-demand service, continuous optimization for idle vehicle relocation is computationally expensive, and thus, not effective. This study proposes a deep learning-based algorithm that can instantly predict the optimal solution to idle vehicle relocation problems under various traffic conditions. The proposed relocation process comprises three steps. First, a deep learning-based passenger demand prediction model using taxi big data is built. Second, idle vehicle relocation problems are solved based on predicted demands, and optimal solution data are collected. Finally, a deep learning model using the optimal solution data is built to estimate the optimal strategy without solving relocation. In addition, the proposed idle vehicle relocation model is validated by applying it to optimize the SAEV system. We present an optimal service system including the design of SAEV vehicles and charging stations. Further, we demonstrate that the proposed strategy can drastically reduce operation costs and wait times for on-demand services.

A Simulation-optimization framework of vehicle relocation for one-way electric carsharing systems

In one-way electric carsharing systems (ECSs), customers can pick up an electric vehicle (EV) at one location and return it to any location, causing an imbalance of EVs and revenue loss for ECSs. In order to address this problem, a simulation-optimization framework is introduced to determine optimal vehicle relocation strategies. In this framework, the discrete event simulation model considers stochasticity and EV-dependent road congestion to simulate the operation of ECSs. And the optimization module is triggered by the simulation model to provide relocation strategies to the operator based on the genetic algorithm with the optimal computing budget allocation. The results of numerical experiments demonstrate that the simulation model can reflect the operation of ECSs in the real world, and the joint relocation strategies achieve improvements in both the profit and the vehicle utilization rate and reduce the number of relocations concerning the single relocation strategies used in the ECS.

A user-based adaptive joint relocation model combining electric car-sharing and bicycle-sharing

This paper presents a user incentive-based adaptive joint relocation model, combining electric car-sharing and bicycle-sharing to optimize the spatiotemporal distribution of shared electric vehicles and increase the use of car-sharing. On the one hand, the optimization model takes subsidy cost and user satisfaction into consideration. On the other hand, multiple dynamic constraints, including the state of charge of electric vehicles, cycling distance, station status, the historical trend of user orders, are considered. Then, the Genetic Algorithm is adapted to solve this joint relocation model; the optimal subsidy price for users, initial vehicle configuration and multi-time-period dynamic thresholds in each station and vehicle relocation scheme are obtained. Finally, a well-known electric vehicle-sharing company is chosen as a case study. The results show that the relocation cost of joint relocation is reduced by around 70% compared with traditional staff relocation, and user satisfaction can be enhanced.

A Data-Driven Approach for Vehicle Relocation in Car-Sharing Services with Balanced Supply-Demand Ratios

A Data-Driven Approach for Vehicle Relocation in Car-Sharing Services with Balanced Supply-Demand Ratios

An innovative approach to solve the carsharing demand-supply imbalance problem under demand uncertainty

This paper studies the demand-supply imbalance problem for one-way carsharing systems under a combination of pricing strategy, relocations and access trips considering stochastic demand. A novel concept of a virtual zone is utilized to capture vehicle relocation range and client walking or biking distance constraints in one-way carsharing systems. The vehicle imbalance problem is further addressed by combining a long-term pricing strategy and real-time vehicle relocations in a two-stage stochastic programming model. In the first stage, the tactical decisions including fleet size and trip price are optimized, while anticipating the operational costs from the second stage. The second stage optimizes operational decisions under uncertain demand including vehicle relocations conditional on the tactical decisions in stage one. The model aims to maximize the profit of a carsharing company considering the fleet costs calculated in stage one and the expected operational costs and revenue obtained in stage two. A dedicated gradient search algorithm is developed to solve the two-stage stochastic programming and results are compared to a genetic algorithm and an iterated local search algorithm. The proposed model and corresponding solution approach are applied to a large-scale network with 50 zones and over 1000 vehicles in Suzhou, China. The application allows us to attain additional operational insight. Results suggest that increased prices for high demand stations during peak hours reduce demand while maintaining profitability of the system. It is also found that the real-time vehicle relocations and flexibility of clients to pick up vehicles at farther stations can increase demand service rate by as much as 10%.