Empty Vehicle Research Articles

A Study of Heavy Road Freight Transport in Poland in the Context of the Pursuit of Sustainable Road Transport

The efficiency of road freight transport determines—to a significant degree—the total environmental footprint and the amount of greenhouse gases and other pollutants released into the atmosphere by inland transport. The rate of empty or partially empty vehicles is one of the key metrics for improvement of the environmental performance of freight road transportation. This paper presents the characteristics of road freight transport in Poland on the basis of data collected by weigh-in-motion stations. Data aggregation for environmental analysis represents a novel aspect of the work. Indicators describing the degree of loading, the share of empty vehicles in traffic, and the share of vehicles of maximum permissible total weight in traffic were determined for a representative group of heavy goods vehicles. Based on the representative load factor value (LFA), a classification of the road section into four groups was proposed. The results obtained show a clear differentiation of the values of the indicators analyzed for individual groups and their variability in the three-year period covered by the analysis. The aggregation method presented can be used to identify the nature of the distribution of the weight categories of the heavy goods vehicles and provide input information for targeted analyses relating to sustainable road transport management and environmental protection. Finally, the grouped LFA values were linked with indicators of energy consumption (ECI) and on-road emissions (EI).

FBrake, a Method to Simulate the Brake Efficiency of Laden Light Passenger Vehicles in PTIs While Measuring the Braking Forces of Their Unladen Configurations

This study introduces fBrake, a novel simulation method now designed for use in periodic technical inspections of M1 and N1 vehicle categories, addressing challenges posed by Directive 2014/45/EU. The directive mandates that braking efficiency must be measured relative to the vehicle’s maximum mass, which often results in underperformance during inspections due to vehicles typically being unladen. This discrepancy arises because the maximum braking forces are proportional to the vertical load on the wheels, causing empty vehicles to lock their wheels prematurely compared to laden ones. fBrake simulates the braking forces of unladen vehicles to reflect a laden state by employing an optimal brake-force distribution curve that aligns with the vehicle’s inherent braking behavior, whether through proportioning valves or through electronic brake distribution systems in anti-lock-braking-system-equipped vehicles. Our methodology, previously applied to heavy vehicles, involved extensive experimentation with a roller brake tester, comparing the actual braking performances of dozens of vehicles to those of their simulated counterparts using fBrake. The results demonstrate that fBrake reliably replicates the braking efficiency of laden vehicles, validating its use as an accurate and effective tool for braking system assessments in periodic inspections, irrespective of the vehicle’s load condition during the test. This approach ensures compliance with regulatory requirements while enhancing the reliability and safety of vehicle inspections.

REVIEW AND ANALYSIS OF THE MAIN DIRECTIONS ON IMPROVING THE EFFICIENCY TRANSPORTATION OF MASS CARGO BY RAIL TRANSPORT

The research found that mass cargo transportation in Ukraine plays a key role in the functioning of the economy, driving the development of many industries and ensuring the country's competitiveness in the global market.Based on the analysis of freight transportation volumes over the recent period, the main trends and factors affecting them were identified. In particular, the growth in rail freight transportation is largely due to the expansion of the sea corridor through the Danube ports. This has made it possible to export Ukrainian products to Europe and other regions of the world, which has stimulated growth in demand for rail transportation. Due to the expansion of the maritime corridor and the change in the geopolitical situation, the share of export cargo transported by rail has increased significantly. These are primarily grain, oilseeds, metallurgical products and chemicals.Induced analyzes scientific research related to the issues of improving the efficiency of mass cargo transportation by rail. Such important problems as the problem of timely provision of loading resources, delays in transportation, inefficient use of empty vehicles, loss of cargo during transportation, etc. are considered. Ways to solve these problems, which are considered in the works of scientists, include infrastructure modernization, introduction of new technologies and optimization of logistics processes.The question was raised the issue of the efficiency of using container and containerized transportation technology. Their advantages, such as reduction of time and costs for transportation, environmental friendliness, are shown, and their disadvantages are considered.The mathematical model of the movement of a container train is analyzed and the feasibility of creating dry port-type structures for optimizing the transportation of mass cargo is investigated.These researches point to the need for a systematic approach to improving the efficiency of mass freight transportation by rail, taking into account various aspects, including technological, infrastructural, logistical and organizational aspects. Such an approach will allow achieving a higher level of efficiency and competitiveness of rail transport in the transportation of mass cargo.Proposed measures for improve the efficiency of mass cargo transportation by rail: optimization of routes, increasing energy efficiency and carrying capacity of railcars, modernization of infrastructure, use of the latest technologies, optimization of loading and unloading, and improvement of safety and quality of service.

A hierarchical control framework for vehicle repositioning in ride-hailing systems

This paper introduces a multi-layer control strategy for efficiently repositioning empty ride-hailing vehicles, aiming to bridge the gap between proactive repositioning strategies and micro-management. The proposed framework consists of three layers: an upper-layer employing an aggregated model based on the Macroscopic Fundamental Diagram (MFD) and model predictive control (MPC) to determine optimal vehicle repositioning flows between each pair of regions, a middle-layer converting macroscopic decisions into dispatching commands for individual vehicles, and a lower-layer utilizing a coverage control algorithm for demand-aligned positioning guidance within regions. The upper-layer contributes to the proposed framework by providing a global (macroscopic) view and predictive capabilities including traffic and congestion features. The middle-layer contributes by ensuring and optimal assignment of repositioning vehicles, considering the decision from the upper- and lower- layers. Finally, the lower-layer contributes with operational details at the intersection or node level providing the precision required for microscopic vehicle guidance. Experimental validation using an agent-based simulator on a real network in Shenzhen confirms the effectiveness and efficiency of the framework in improving empty vehicle repositioning strategies for ride-hailing services in terms of average passenger waiting time and abandonment rates.

Mathematical Modeling on Integrated Vehicle Assignment and Rebalancing in Ride-hailing System with Uncertainty Using Fuzzy Linear Programming

The general public frequently uses taxis as local transportation to get from one location to another. Ride-hailing is an innovation in taxi services that lets customers use their smartphones to find drivers, find prices, and submit requests. The two parts of ride-hailing are vehicle assignment and rebalancing. The task of the assignment is to allocate resources as efficiently as possible to fulfil demand. Demand and supply in the area are brought into balance through the rebalancing process. The rebalancing step is frequently carried out independently of the assignment process in ride-hailing pickup systems. However, it can be integrated into a single optimization process to improve system performance. The linear programming approach can integrate assignment and rebalancing. The batch assignment is an assignment algorithm in which each supply and demand is collected within a specific time window. The assignment is done after the vehicles and requests are collected. Fuzzy linear programming is used to deal with environmental uncertainty. Uncertain demand can reduce the reliability of ride-hailing pickup systems in addressing the problem of allocating empty vehicles to areas of high demand. The pickup time may change over time due to traffic conditions. Assignment integration and rebalancing modelling with fuzzy parameters are carried out to obtain a ride-hailing pickup system model that can handle demand uncertainty, pickup travel time, and travel delay times and increase the ride-hailing effectiveness of the pickup system. Numerical simulations were carried out on the assignment and rebalancing integration model without and with uncertainty parameters—numerical simulations based on publicly available taxi travel request data. The model selection for the ride-hailing system can be determined based on the numerical simulation results. This research can potentially advance and expand knowledge, especially in transportation.

Study on the Mechanism of Wheel Asymmetric Wear of Heavy-Haul Freight Vehicles

Wear on the wheel-rail interface has a serious effect on the operation safety and efficiency of heavy-haul freight vehicles. Wheel asymmetric wear (WAW) greatly affects the vehicle’s dynamic performance. This paper explores the causes of WAW in heavy-haul freight vehicles and proposes the limit value of the WAW. A dynamic model of heavy-haul freight vehicle is established and the Jendel wheel wear model is used to analyze the mechanisms and influencing factors of WAW. The study primarily focuses on four factors: uneven loading of freight vehicles, differences in wheel surface hardness, suspension errors of the left and right side, and the distribution characteristics of the line. Next, the wheel wear model is verified by the test data, the flange wear and tread wear are analyzed at the same time. The WAW limits value under the load and empty conditions by considering the elastic wheelset model is investigated. The results showed that differences in wheel surface hardness between the left and right wheels and line distribution are the main causes of WAW. By conducting dynamic calculations, the WAW limit value of the empty and heavy vehicle is set as 2 mm. During the operation, it is necessary to effectively monitor the vehicle and track parameters that affect WAW.

Non-Viral RNA-Lipid Nanoparticles for High Efficiency Genome Editing of CD34+ Hematopoietic Stem and Progenitor Cells for Advanced Cell and Gene Therapies

Background and Aims: The application of CRISPR-Cas9 gene editing in hematopoietic stem (HSC) and progenitor cells (HSPCs) has the potential to revolutionize the treatment of genetic blood disorders by correcting disease-causing mutations. Previously, we demonstrated the utility of a novel lipid nanoparticle (LNP) reagent for the multiplexed engineering of gene-edited CAR T cells, showing high cell viabilities, rapid onset editing, and potent CAR-mediated killing. In this current work, we demonstrate the capability of LNPs for the efficient and gentle delivery of genetic material to CD34+ HSPCs. The benefits of a non-viral LNP-mediated approach paves the way for the development of next generation gene therapy products in the HSC field. Methods: LNPs encapsulating S.p. Cas9 mRNA and CD33 or CD45 targeted guide RNA (sgRNA) were produced using the scalable NanoAssemblr® NxGen™ microfluidic platform technology ( Fig. 1A). The LNP composition was optimized for the gentle and efficient cargo delivery to HSC/HSPCs. Purified human CD34+ cells were cultured, stimulated, and treated by the direct addition of the RNA-LNPs. Various commercial cell sources were tested including mobilized peripheral blood and cord blood. Gene knockout and viability were assessed using flow cytometry, cell proliferation using an automated cell counter, and differentiation capacity by colony-forming unit (CFU) assays. Results: One-step addition of LNPs to CD34+ HSPCs resulted in 84 ± 6% CD33 and 81 ± 2% CD45 knockout efficiencies (n=6 CD34+ donors, Fig. 1B). After treatment, cells maintained on average 95 ± 3% absolute cell viability, compared to 99% viability of the untreated cells, Fig. 1C. Furthermore, LNP treated HSCs maintained excellent cell proliferation, with over >90% relative cell proliferation to the untreated controls. The CFU assays showed no significant change in relative lineage formation for both the RNA-LNPs and the empty LNP vehicle control ( Fig. 1D). Finally, LNP production was successfully scaled-up using microfluidics from discovery to pre-clinical scales. Conclusions: LNP-mediated CRISPR-Cas9 mRNA delivery is a promising approach for gene editing in HSPCs. The simple and gentle nature of LNP cell treatment allows for multiple genetic engineering steps for simultaneous expression and deletion of proteins for novel gene therapies. Furthermore, LNPs can be easily manufactured using microfluidics, enabling small-scale screening of RNA libraries and rapid scale-up for clinical translation.

Optimization of Green Vehicle Paths Considering the Impact of Carbon Emissions: A Case Study of Municipal Solid Waste Collection and Transportation

In recent years, the waste produced as a result of the production and consumption activities of urban residents has led to significant environmental degradation and resource wastage. This paper focuses on the research object of municipal solid waste (MSW) collection and transportation based on the concept of “sustainable development and green economy”. Firstly, this study examines the current state of urban domestic garbage collection and transportation. It analyzes the following challenges and deficiencies of the existing collection and transportation system: (1) the operating efficiency of garbage collection vehicles is low, resulting in a significant accumulation of waste on the roadside and within the community; (2) the vehicle collection and transportation routes are fixed, and there are empty vehicles running; (3) the amount of garbage on a route exceeds the vehicle’s loading capacity, which requires the vehicle to perform a second round of collection and transportation. To enhance the efficiency of urban garbage collection and transportation and minimize the collection and transportation costs, we are investigating the problem of optimizing the path for green vehicles. To comprehensively optimize the fixed cost, variable cost, and carbon emission cost incurred during vehicle operation, a vehicle routing model with time windows is established, taking into account vehicle load constraints. Carbon emission coefficient and carbon tax parameters are introduced into the model and the “fuel-carbon emission” conversion method is used to measure the carbon cost of enterprises. An improved ant colony optimization (ACO) method is proposed: (1) the introduction of a vehicle load factor improves the ant state transfer method; (2) the updated pheromone method is improved, and additional pheromone is added to both the feasible path and the path with the minimum objective function; (3) the max–min ACO algorithm is introduced to address the issue of premature convergence of the algorithm; (4) the embedding of a 2-opt algorithm further prevents the ACO algorithm from falling into the local optimum. Finally, the calculation results based on the example data demonstrate that the algorithm has a significant advantage over the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The total transportation distance determined by this algorithm is shorter than that of the GA and PSO methods, and the total cost of the scheme is 1.66% and 1.89% lower than that determined by GA and PSO, respectively. Compared to the data from the actual case, the number of vehicles required in the operation of this algorithm and model is reduced by three. Additionally, the total cost, fixed cost, and carbon emission cost incurred by the vehicles during operation were reduced by 31.2%, 60%, and 25.3% respectively. The results of this study help the station to collect and distribute waste efficiently, while also achieving the goals of energy saving, consumption reduction, and emission reduction.

Estimating the impact of low temperature on African swine fever virus transmission through contaminated environments.

African Swine Fever Virus (ASFV) is the cause of an infectious disease in pigs, which is difficult to control. Long viability of ASFV has been shown for several contaminated materials, especially under low temperature. Therefore, when pigs are exposed to a contaminated environment, new infections could occur without the presence of infectious individuals. For example, a contaminated, poorly washed, empty livestock vehicle poses a risk to the next load of pigs. A quantitative stochastic environmental transmission model was applied to simulate the change in environmental contamination levels over time and calculate the epidemic parameters through exposure-based estimation. Due to the lack of experimental data on environmental transmission at low temperatures, we performed a non-linear fit of the decay rate parameter with temperature based on a literature review. Eventually, 16 scenarios were constructed for different temperature (at 20°C, 10°C, 0°C, or -10°C) and duration of empty periods (1, 3, 5, or 7 days) after the environment had been contaminated. We quantified the variation in the contamination level of the environment over time and the probability of newly added recipients getting infected when exposed to the environment after the empty period. As a result, the transmission rate parameter for ASFV in pigs was estimated to be 1.53 (0.90, 2.45) day-1, the decay rate parameter to be 1.02 (0.73, 1.47) day-1 (at 21°C), and the excretion rate parameter to be 2.70 (2.51, 3.02) day-1. Without washing and disinfecting, the environment required 9, 14, 24, 54 days to reach a low probability of causing at least one new case (<0.005) at 20°C, 10°C, 0°C, -10°C, respectively. In addition, the method proposed in this paper enables assessment of the effect of washing and disinfecting on ASFV environmental transmission. We conducted this study to better understand how the viability of ASFV at different temperatures could affect the infectivity in environmental transmission and to improve risk assessment and disease control strategies.

Vehicle/Employee Rebalancing and Charging Scheduling in One-Way Car Sharing Systems

This paper studies the joint charging scheduling and rebalancing of employees and electric vehicles in one-way car sharing systems (OCS). Consider an OCS consisting of a fleet of vehicles and a team of employees responsible for charging low-battery level vehicles and rebalancing the idle vehicles in the system. The employees need also to rebalance themselves by service vehicles or by sharing a car with some customers. Firstly, a joint optimization method to ensure vehicle and employee balance is proposed, where the variation of the number of vehicles, customers and employees is described by fluid models, while the charging process of electric vehicles at charging stations is modeled by an M/M/S queue. Then, the well-posedness and equilibrium of the fluid model are proven, and the minimum fleet size and employee size for the system to reach equilibrium is given. Next, a rebalancing method is presented to minimize the number of empty vehicles and rebalancing employees traveling in the network. Numerical experiment and case study show that the proposed method can significantly improve the quality of service and reduce the number of rebalancing employees.

A case-oriented computational study for sustainable fleet planning in a battery closed-loop supply chain network under uncertainty

PurposeThe purpose of this study is to develop a holistic optimization model for an integrated sustainable fleet planning and closed-loop supply chain (CLSC) network design problem under uncertainty.Design/methodology/approachA novel mixed-integer programming model that is able to consider interactions between vehicle fleet planning and CLSC network design problems is first developed. Uncertainties of the product demand and return fractions of the end-of-life products are handled by a chance-constrained stochastic program. Several Pareto optimal solutions are generated for the conflicting sustainability objectives via compromise and fuzzy goal programming (FGP) approaches.FindingsThe proposed model is tested on a real-life lead/acid battery recovery system. By using the proposed model, sustainable fleet plans that provide a smaller fleet size, fewer empty vehicle repositions, minimal CO2 emissions, maximal vehicle safety ratings and minimal injury/illness incidence rate of transport accidents are generated. Furthermore, an environmentally and socially conscious CLSC network with maximal job creation in the less developed regions, minimal lost days resulting from the work's damages during manufacturing/recycling operations and maximal collection/recovery of end-of-life products is also designed.Originality/valueUnlike the classical network design models, vehicle fleet planning decisions such as fleet sizing/composition, fleet assignment, vehicle inventory control, empty repositioning, etc. are also considered while designing a sustainable CLSC network. In addition to sustainability indicators in the network design, sustainability factors in fleet management are also handled. To the best of the authors' knowledge, there is no similar paper in the literature that proposes such a holistic optimization model for integrated sustainable fleet planning and CLSC network design.

Maximum Throughput Dispatch for Shared Autonomous Vehicles Including Vehicle Rebalancing

Shared autonomous vehicles (SAVs) provide on demand point-to-point transportation for passengers. This service has been extensively studied using dispatch heuristics and agent based simulations of large urban areas. However, these approaches make no mathematical guarantees of the passenger throughput for the SAV network. This study builds on the dynamic queuing model design of Kang and Levin which provides a maximum stability dispatch policy for SAVs. This model is extended to include rebalancing of empty vehicles to regions of high demand. The modified dispatch policy is proven to maximize throughput. Simulation results show that this dispatch policy reduces waiting times (between vehicle dispatch and passenger pickup) compared to the original formulation. However, vehicle time traveling empty increases in some scenarios. Simulation results also show that rebalancing often reduces passenger waiting times, but not when too many vehicles rebalance at once and are not available for dispatch.

A predictive chance constraint rebalancing approach to mobility-on-demand services

This paper considers the problem of supply-demand imbalances in Mobility-on-Demand (MoD) services. These imbalances occur due to uneven stochastic travel demand and can be mitigated by proactively rebalancing empty vehicles to areas where the demand is high. To achieve this, we propose a method that takes into account uncertainties of predicted travel demand while minimizing pick-up time and rebalance mileage for autonomous MoD ride-hailing. More precisely, first travel demand is predicted using Gaussian Process Regression (GPR) which provides uncertainty bounds on the prediction. We then formulate a stochastic model predictive control (MPC) for the autonomous ride-hailing service and integrate the demand predictions with uncertainty bounds. In order to guarantee constraint satisfaction in the optimization under estimated stochastic demand prediction, we employ a probabilistic constraining method with user-defined confidence interval, using Chance Constrained MPC (CCMPC). The benefits of the proposed method are twofold. First, travel demand uncertainty prediction from data can naturally be embedded into the MoD optimization framework, allowing us to keep the imbalance at each station below a certain threshold with a user-defined probability. Second, CCMPC can be relaxed into a Mixed-Integer-Linear-Program (MILP) and the MILP can be solved as a corresponding Linear-Program, which always admits an integral solution. Our transportation simulations show that by tuning the confidence bound on the chance constraint, close to optimal oracle performance can be achieved, with a median customer wait time reduction of 4% compared to using only the mean prediction of the GPR.

Optimization of container transportation between multi terminals based on carrier cooperation

To optimize the inter-terminal transportation (ITT) problem, trucks transporting containers from one terminal to another are likely to have the situation of "Full load to empty vehicle return" or "empty vehicle to full load return", a container transportation method proposed in which container carriers. Taking the maximization of the carrier's profit as the goal, considering the terminal operation’s busy degree, the task volume between terminals, and the carrier’s number, a container transportation task allocation model based on carrier cooperation is established. The analysis and comparison between the carrier's "cooperation" strategy and the "non-cooperation" strategy show that if multiple container truck carriers cooperate by sharing trucks and orders, The total profit will increase by 32.3% and the number of trucks will decrease by 25.7%.

Modular vehicle routing for combined passenger and freight transport

The continuous increase in urban deliveries and the ongoing urbanization of large cities require the development of efficient and sustainable transportation solutions. This study investigates the impact of modular vehicle concepts and the consolidation of different demand types in the route planning on the efficiency of the urban freight and passenger transportation system. Modularity is achieved by connecting multiple vehicles together to form a platoon. The consolidation of different demand types is realized by simultaneously consider passenger and freight demand in the optimization algorithm. The considered vehicles are specific for each demand type by can be connected freely, hence it is possible to transport different demand types in the same platoon. The cost terms in the problem formulation are comprised of travel time costs, travel distance costs, fleet size costs, and cost considering unserved requests. The modular vehicle operations are modeled in a novel pickup and delivery problem which is solved using CPLEX and Adaptive Large Neighborhood Search. In an extensive scenario study and case study in Stockholm, the potentials of the new modular vehicle type are explored for different spatial and temporal demand distributions. A parameter study on vehicle capacity, vehicle range and cost saving assumptions is performed to study their influence on the efficiency. The experiments carried out indicate a general cost savings of 48% due to modularity and an additional 9% due to consolidation. The reduction mainly stems from reduced operating costs and reduced trip duration, while the same number of requests can be served in all cases. Empty vehicle kilometers are reduced by more than 60% by consolidation and modularity. The proposed model and optimization framework can be used by companies and policy makers to identify required fleet sizes, optimal vehicle routes and cost savings.

Anticipatory rebalancing of RoboTaxi systems

This study considers a RoboTaxi service, a futuristic transportation system whereby passengers can place transportation requests in real-time for a private ride. The passengers are picked up from their origins as soon as possible after placing a request and travel directly to their destination, from which the RoboTaxi may immediately continue to serve other customers, remain idle, or move to another location in the city in anticipation of future demand.The operation of RoboTaxi systems requires online decisions regarding vehicle dispatching and rebalancing. Dispatching assigns cars to requests. Rebalancing is the initiation of empty trips of idle cars to reduce future requests’ waiting time and take advantage of off-peak traffic hours. This study proposes an advanced method for rebalancing while assuming a simple yet effective dispatching policy.The optimal operation of a RoboTaxi fleet aims to minimize two competing values: the passengers waiting time between requests and pickups and the total distance traveled by empty vehicles. In a RoboTaxi system, vehicles may travel empty either when deadheading to pick up a passenger or while making rebalancing trips. There is a trade-off between the rebalancing effort, which includes various types of costs, and the passengers’ waiting time, representing the quality of the service provided by the system.

Fleet sizing and charging infrastructure design for electric autonomous mobility-on-demand systems with endogenous congestion and limited link space

Autonomous vehicles are to revolutionize the way urban mobility demands are served, and they are most likely to be powered by electricity. To accurately quantify the benefits of replacing existing mobility services with autonomous electric vehicles, electric autonomous mobility-on-demand (EAMoD) systems need to be evaluated and designed with the congestion effects they may cause taken into account. In this work, the congestion effects are depicted through a discrete event simulation model with state-dependent link travel speed and limited link space that allow endogenous congestion to emerge, spread, and dissipate across the entire road network. Three mathematical models are integrated into the simulation model to optimally match vehicles with waiting requests, relocate empty vehicles to potential high-demand areas, and assign low charge vehicles to charging stations based on the workloads of the charging stations. Based on the simulation model, we apply Bayesian optimization to jointly design the fleet size and charging facility configuration considering the endogenous congestion incurred by the daily operation of autonomous electric vehicles. Contraction hierarchies are adopted to route vehicles to perform assigned tasks in real-time. The proposed solution method is tested on Manhattan below 60th street, which corresponds to the potential congestion pricing zone in New York City. Experiment results show that the proposed simulation-based optimization approach is computationally tractable, and can find a satisfactory solution to the fleet sizing and charging infrastructure design problem within a tight computation budget. Excluding congestion effects at system design stage would lead to up to 14% passenger loss due to long assignment waiting time. Although the charging facility cost accounts for only 1.8% of the total cost, the number of chargers can indirectly affect the percentage of passengers that can be served through its efficiency in recharging vehicles. A charging facility configuration aligned with the fleet size can help to improve service quality and vehicle utilization rate.

Identifying the collaborative scheduling areas between ride-hailing and traditional taxi services based on vehicle trajectory data

Traditional taxi services (TTSs) play an important role in satisfying daily travel demands. The rapid growth of ride-hailing services (RHSs) has increased the convenience of customized travel. However, the volume of empty ride-hailing vehicles has increased. Identifying the collaborative scheduling areas (CoSAs) between RHSs and TTSs can further improve the efficiency of urban travel services. Therefore, we propose a method to identify CoSAs based on the travel demand and vehicle supply of TTSs and RHSs derived from trajectory data. We first optimize and make the temporal resolution of the trajectories of different types of vehicles uniform based on the shortest path algorithm. Then, the indicators describing travel demand and vehicle supply are defined and calculated. Finally, the areas with a high vehicle supply of one type and a low vehicle supply and high travel demand of the other type are identified as CoSAs. A dataset for Xiamen Island indicates that the CoSAs of TTSs to RHSs can provide potential routes to pick up passengers that are 41% and 11% shorter than the actual routes at 9:00 and 18:00, respectively. The constructed method can also improve the CoSA identification results.

Addressing the urban-scale vehicle assignment and rebalancing problems in shared autonomous vehicle system while simultaneously considering immediate, reservation, shareable, and unshareable requests

Recently, the autonomous driving technology has developed rapidly with the shared autonomous vehicle system extensively investigated. However, little attention has been given to the diversity of the travel demands. In view of this, the paper proposes a dispatching strategy to solve the vehicle assignment and rebalancing problems with simultaneous consideration of immediate, reservation, shareable and unshareable requests. The centralised optimisation and the decentralised heuristic methods are combined to handle the different requests efficiently, which enables the dispatching strategy to address the urban scale problem. The centralised optimisation method assigns the short-term requests and plans the empty vehicle rebalance by the network simplex algorithm, while the decentralised heuristic method assigns the long-term requests and fully uses the previous step results in each solution step of the rolling horizon. The simulation of Manhattan, New York, with 4,000 vehicles and 1,823,152 requests, confirms the high operational performance and the efficiency of the proposed dispatching strategy compared to the existing approach and the benchmark strategy. It was also found that more reservation requests with the earlier reservation time did not generate significant improvement in operational performance, and the high sharable request ratio facilitated the reduction of the travel distance and empty distance.

PGDRT: Prediction Demand Based on Graph Convolutional Network for Regional Demand-Responsive Transport

To provide an efficient demand-responsive transport (DRT) service, we established a model for predicting regional movement demand that reflects spatiotemporal characteristics. DRT facilitates the movement of restricted passengers. However, passengers with restrictions are highly dependent on transportation services, and there are large fluctuations in travel demand based on the region, time, and intermittent demand constraints. Without regional demand predictions, the gaps between the desired boarding times of passengers and the actual boarding times are significantly increased, resulting in inefficient transportation services with minimal movement and maximum costs. Therefore, it is necessary to establish a regional demand generation prediction model that reflects temporal features for efficient demand response service operations. In this study, a graph convolutional network model that performs demand prediction using spatial and temporal information was developed. The proposed model considers a region’s unique characteristics and the influence between regions through spatial information, such as the proximity between regions, convenience of transportation, and functional similarity. In addition, three types of temporal characteristics—adjacent visual characteristics, periodic characteristics, and representative characteristics—were defined to reflect past demand patterns. With the proposed demand forecasting model, measures can be taken, such as having empty vehicles move to areas where demand is expected or encouraging adjustment of the vehicle’s rest time to avoid congestion. Thus, fast and efficient transportation satisfying the movement demand of passengers with restrictions can be achieved, resulting in sustainable transportation.