Vehicle Capacity Research Articles

Optimal Vehicle Scheduling and Charging Infrastructure Planning for Autonomous Modular Transit System

Prioritizing the development of public transport is an effective way to improve the sustainability of the transport system. In recent years, bus passenger flow has been declining in many cities. How to reform the operating mode of the public transportation system is an important issue that needs to be solved. An autonomous modular bus (AMB) is capable of physical coupling and uncoupling to flexibly adjust vehicle capacity as well as provide high-quality service under unbalanced passenger demand conditions. To promote AMB adoption and reduce the operating cost of the bus route, this paper presents a joint optimization method to simultaneously determine the AMB dispatching plan, charging plan, and charging infrastructure configuration scheme. Then, a mixed-integer programming model is formulated to minimize the operating costs of the bus route. A hybrid intelligent algorithm combining enumeration, cloning algorithm, and particle swarm optimization algorithm is designed to resolve the formulated model. Subsequently, an actual bus route is taken as an example to validate the proposed method. Results indicate that the developed method in this paper can reduce the operating costs and operational energy consumption of the route compared with the real route operating plan. Specifically, the reduction ratio of the former is 23.85%, and the reduction ratio of the latter is 5.92%. The results of this study validate the feasibility and advantages of autonomous modular transit service, contributing positively to the sustainable development of the urban public transportation system.

Flexible optimal bus‐schedule bridging for metro operation‐interruption

AbstractUnder the sudden interruption of the metro, it is significant to dispatch emergency bridging buses to evacuate stranded passengers to improve linkage management and service reliability. Aiming at the problem of emergency bridging bus scheduling, considering the remaining capacity of conventional buses, passenger tolerance, and site convenience, a flexible combined emergency bridging bus scheduling model is constructed based on the constraints of vehicle capacity, dispatching capacity, and maximum evacuation times of emergency bridging bus, aiming at minimizing the maximum evacuation time and passenger delay. The improved Harris hawk algorithm is used to solve the model, and the evacuation plan of the demand‐responsive and station‐station bridging lines is obtained. The maximum evacuation time is 41 min, and the average delay of stranded passengers is 19 min. The results show that the maximum evacuation time is 10% and 27% less than the fixed combined and single scheduling. The average delay is 16% and 42% less than the fixed combination and traditional single scheduling. The sensitivity analysis of the influencing factors of emergency bridging bus scheduling is conducted. The results show that the flexible combined emergency bridging bus scheduling model constructed in this paper can improve evacuation efficiency and reduce passenger travel delays.

Dynamic, fair, and efficient routing for cooperative autonomous vehicle fleets

This paper addresses challenges in agricultural cooperative autonomous fleet routing through the proposition, modeling, and resolution of the Dynamic Vehicle Routing Problem with Fair Profits and Time Windows (DVRP-FPTW). The aim is to dynamically optimize routes for a vehicle fleet serving tasks within assigned time windows, emphasizing fair and efficient solutions. Our DVRP-FPTW accommodates unforeseen events like task modifications or vehicle breakdowns, ensuring adherence to task demand, vehicle capacities, and autonomies. The proposed model incorporates mandatory and optional tasks, including optional ones in operational vehicle routes if not compromising the vehicles’ profits. Including asynchronous and distributed column generation heuristics, the proposed Multi-Agent-based architecture DIMASA for the DVRP-FPTW dynamically adapts to unforeseen events. Systematic Egalitarian social welfare optimization is used to iteratively maximize the profit of the least profitable vehicle, prioritizing fairness across the fleet in light of unforeseen events. This improves upon existing dynamic and multi-period VRP models that rely on prior knowledge of demand changes. Our approach allows vehicle agents to maintain privacy while sharing minimal local data with a fleet coordinator agent. We propose publicly available benchmark instances for both static and dynamic VRP-FPTW. Simulation results demonstrate the effectiveness of our DVRP-FPTW model and our multi-agent system solution approach in coordinating large, dynamically evolving cooperative autonomous fleets fairly and efficiently in close to real-time.

Spectrally Efficient UAV Communications using Non-Orthogonal Multiple Access (NOMA)

Non-orthogonal multiple access (NOMA) scheme with its share of benefits such as spectral efficiency and simultaneous support of massive connectivity, is one of the keys enabling technologies for upcoming 5G. However, in the context of UAV-assisted communication, existing literature mainly focuses on conventional orthogonal multiple access (OMA) which limits the spectral efficiency and number of simultaneous connections. Existing technologies are using LTE that only can serve 1 user per resource block. The user capacity nowadays is increased, so the existing technologies should be improved to higher user capacity such as 4 users per resource block. This research project focuses on the design of NOMA scheme for multiple users aiming to maximize the system capacity and spectral efficiency of Unmanned Aerial Vehicle (UAV) hotspot with baseline model and to simultaneously support mobile users. The proposed NOMA multi-users model is then compared with baseline model of 2 users per resource block in NOMA and OMA. The proposed NOMA model increases spectral efficiency, higher throughput and improved user fairness. The project only focuses on downlink communication with stationary users. The parameters measured are Bit Error Rate, Sum Rate, and Spectral Efficiency that depend on power allocation, altitude of UAV, and distance between UAV and users. Based on the simulation results, NOMA which employs Fractional Transmit Power Allocation (FTPA) algorithm has better performance in terms of sum-rate and spectral efficiency compared to those of OMA by as much as 650%. Results are also presented for various NOMA power allocation algorithms.

Integrating parcel delivery schedules with public transport networks in urban co-modality systems

Co-modality transportation advocates using urban public transport to support urban freight operations. This study considers the implementation of co-modality in a fixed-route transit network comprising multiple lines following predetermined routes and schedules. We first develop a schedule-based parcel assignment model to formulate the synchronized co-modality transportation problem (SCTP). The effectiveness of the proposed arc-based meta-heuristic algorithm is substantiated through a comprehensive computational analysis, comparing its performance with that of an exact approach and genetic algorithm. Our findings reveal a nuanced trade-off between transportation efficiency and co-modal stop utilization, identifying a threshold beyond which additional stops do not improve efficiency but increase costs. We also discover a 'buckets effect' in co-modal capacities, suggesting that balanced vehicle and stop capacities are crucial for optimizing system performance. A case study with real urban transit data validates our model's potential for significant efficiency gains in co-modality transportation systems, offering actionable insights for urban logistics.

Electric vehicle supply equipment location and capacity allocation for fixed-route networks

Electric vehicle (EV) supply equipment location and allocation (EVSELCA) problems for freight vehicles are becoming more important because of the trending electrification shift. Some previous works address EV charger location and vehicle routing problems simultaneously by generating vehicle routes from scratch. Although such routes can be efficient, introducing new routes may violate practical constraints, such as drive schedules, and satisfying electrification requirements can require dramatically altering existing routes. To address the challenges in the prevailing adoption scheme, we approach the problem from a fixed-route perspective. We develop a mixed-integer linear program, a clustering approach, and a metaheuristic solution method using a genetic algorithm (GA) to solve the EVSELCA problem. The clustering approach simplifies the problem by grouping customers into clusters, while the GA generates solutions that are shown to be nearly optimal for small problem cases. A case study examines how charger costs, energy costs, the value of time (VOT), and battery capacity impact the cost of the EVSELCA. Charger equipment costs were found to be the most significant component in the objective function, leading to a substantial reduction in cost when decreased. VOT costs exhibited a significant decrease with rising energy costs. An increase in VOT resulted in a notable rise in the number of fast chargers. Longer EV ranges decrease total costs up to a certain point, beyond which the decrease in total costs is negligible.

Improving the Profitability of Public Transport by Daily Forecasting of Passenger Loads and Bus Trip Distribution

The degree of utilization of passenger vehicle capacity affects the economic component of public transport operation. This is due to the fact that it depends on the number of passengers transported, as well as on the capacity of the passenger vehicle used for transportation. One of the tasks formulated in the state program of transport development of the Republic of Belarus is to achieve full self-sufficiency of public transport operation. One of the ways to achieve this goal can be maximizing the degree of use of passenger vehicle capacity by ensuring its compliance with the existing parameters of passenger flow. In this paper, based on the values of passenger flows in one of the cities of Belarus, obtained as a result of field observations in 2019 and 2024, we calculate an indicator that assesses the degree of utilization of passenger vehicle capacity, the factors that determine it, and the regularities of its change. Based on such calculations, the feasibility of creating a new public transport management technology based on daily forecasting of passenger traffic and distribution of passenger vehicles of different capacity to the routes taking this into account is shown.

Vehicle routing with stochastic demand, service and waiting times — The case of food bank collection problems

Food banks play an important role both in combating food waste, and in alleviating hunger. However, due to the many uncertainties that food banks face, they often struggle to effectively collect all food items that donors such as supermarkets are willing to provide. To tackle this problem, we introduce the capacitated vehicle routing problem with travel time restrictions and stochastic demand, service and waiting times, in which the uncertainties are dependent of each other. This problem can be generalized to a large variety of routing applications. The goal of the problem is to determine a minimum number of vehicles, and to plan cost-effective routes for these vehicles so that each route violates the vehicle capacity and the travel time limit with only a very small probability. The resulting problem is highly complex and thus solved by means of a matheuristic, which decomposes the problem into its natural decision components. Thus, it first determines the number of districts into which the service area should be partitioned, before allocating each customer to exactly one district and then plans a route for each district. A set of feedback mechanisms is activated whenever no feasible solution has been found through these steps. Extensive numerical experiments, involving both randomly generated and real-life instances, demonstrate the matheuristic’s effectiveness in solving instances with up to 100 customers. When applying our matheuristic to real-life instances from Dutch and Canadian food banks, we furthermore gain managerial insights to assist in optimizing fleet size and route cost.

Cost optimization in the urban distribution of dairy supply – A case of Sanchi, Bhopal, India

ABSTRACT The study focuses on optimizing urban distribution in the dairy industry, specifically at Bhopal Sanchi dairy, to enhance efficiency and minimize costs. It evaluates existing routes, identifies inefficiencies, and proposes new distribution strategies. By implementing these changes, significant improvements are achieved, reducing total distribution costs and dead kilometers while improving vehicle capacity utilization. The study emphasizes the importance of addressing urban distribution challenges to meet increasing milk demand efficiently. It contributes to developing sustainable dairy distribution systems by optimizing costs and minimizing losses. ArcGIS software is utilized for route and fleet optimization analysis. Overall, the research addresses the critical need for efficient urban dairy distribution, particularly in India’s context as a major milk producer.

Methodology of calculation of battery capacity of mobile vehicle with electric drive on the basis of T16M

Methodology of calculation of battery capacity of mobile vehicle with electric drive on the basis of T16M

Sustainable waste collection optimization using electric vehicles

Solid waste management is a crucial municipal service directly influencing urban livability. Optimizing refuse collection routes enhances service efficiency and reduces costs. The recent introduction of fully electric refuse trucks, boasting features like zero emissions, low noise, and lower operating costs compared to diesel trucks, holds promise. However, their limited autonomy presents challenges that need addressing before widespread deployment. This study proposes a realistic formulation for the refuse collection routing problem with electric trucks, introducing electromobility constraints never considered before in related works. The goal is to find optimal routes covering all collection points while minimizing travel distance within vehicle battery and capacity limits. An accurate energy consumption model is presented, accounting for route elevation, vehicle speed, waste collection operations, and regenerative braking. Genetic algorithms are used to find solutions to the problem, as well as the well-known Clarke and Wright heuristic for comparison purposes. The algorithms are tested on six diverse instances reflecting real-world scenarios in New York City (United States) and Puerto Real (Spain). Results indicate the genetic algorithm’s superiority, yielding solutions with reduced travel distance and consumption while requiring fewer routes compared to the state-of-the-art heuristic.

A novel model for transfer synchronization in transit networks and a Lagrangian-based heuristic solution method

To realize the benefits of network connectivity in transfer-based transit networks, it is critical to minimize transfer disutility for passengers by synchronizing timetables of intersecting routes. We propose a mixed-integer linear programming timetable synchronization model that incorporates new features, such as dwell time determination and vehicle capacity consideration, which have been largely overlooked in the literature at the scheduling stage. We introduce a new concept of pre-planned holding time, called transfer buffer time, to reduce the transfer waiting time, particularly for transfers to low-frequency routes, while taking into account the penalty of extra in-vehicle time for onboard passengers and the possible consequences on headway regularity of a route. We develop a Lagrangian relaxation-based heuristic to obtain high-quality solutions efficiently for large instances. Our experiments on instances with up to 12 transfer nodes in the City of Toronto, with a mixture of low- and high-frequency routes, illustrate the potential benefits of the proposed model over the state of the art. The results indicate that incorporating transfer buffer time, dwell time determination, and vehicle capacity consideration improves model outcomes considerably, also demonstrating the computational efficiency of our Lagrangian-based solution method.

Active learning algorithms for autonomous platforms to characterize underwater static acoustic sources

Integrating passive acoustics with autonomous platforms presents an opportunity to complement the spatial constraints of traditional fixed sensor methods by leveraging the capacity of autonomous underwater vehicles (AUVs) to make real-time decisions. We present algorithms that adaptively sample a survey region based on the sound field characteristics. These algorithms use an active learning strategy based on Gaussian Process (GP) regression to characterize a static sound field in a survey region. With each location sampled, the algorithms employ a GP to estimate the distribution and quantify the uncertainty of static acoustic sources within the region. The uncertainty metric is used to then choose the next sampling location. This dynamic approach not only maximizes the information gained by the AUV at every location that it samples but also ensures an efficient convergence toward the true distribution of underwater static sources in that region. These algorithms were developed in simulation and will be tested in controlled experiments.

Re-envisioning the Park-and-Ride concept for the automated vehicle (AV) era with Private-to-Shared AV transfer stations

Cities implemented park-and-ride (PNR) systems to decrease congestion in dense urban areas while providing transit options to travelers who live in a city's low- to medium-density regions. The success of PNR systems is mixed, as they suffer from several disadvantages, namely, the uncertainty of parking locations and infrequent and/or unreliable transit services, and the fact that travelers still need to walk to their destination. Motivated by the premise of PNR systems and the potential of automated vehicles (AVs), to address each of the shortcomings of PNR systems, this study proposes a future system with near-ubiquitous AVs where travelers transfer from privately owned AVs (PAVs) to shared-use, shared-ride AVs (SAVs), called a PAV-SAV transfer system. This study proposes a modeling framework to assess the potential market share of the PAV-SAV transfer system and the network impacts (e.g., congestion, vehicle miles traveled) of the proposed system. Finally, the study identifies good designs for the PAV-SAV transfer system using scenario analysis. The critical design variables are the location of transfer stations, the capacity of SAVs, and the transfer station connector links. For the Greater Los Angeles area, the computational results show a market share for PAV-SAV of almost 18% for person trips terminating in downtown Los Angeles. In all scenarios, the proposed PAV-SAV system decreases vehicle hours traveled (VHT) across the whole network with significant decreases in the urban core. For all designs, the PAV-SAV system decreases vehicle miles traveled (VMT) compared to a network without PAV-SAV transfer stations, albeit only slightly. Locating transfer stations closer to the urban core, increasing vehicle capacities, and connecting transfer stations to both arterial links and highway links improves network performance (i.e., VMT and VHT) and increases the market share of the PAV-SAV system.

An exact algorithm for unpaired pickup and delivery vehicle routing problem with multiple commodities and multiple visits

This paper addresses a new multi-commodity unpaired pickup and delivery vehicle routing problem in which each vehicle is allowed to visit each customer more than once for both pickup and delivery of each commodity. It is a complex variant of the classical capacitated vehicle routing problem and comes from resource transfer among different plants in a distributed manufacturing environment. Notably, the pairing decisions of pickup and delivery for each commodity and the routing decisions among customers are interdependent, and they need to be made simultaneously and collaboratively. Firstly, a mixed integer programming model is developed, and then new valid inequalities are derived to improve the linear programming relaxation. Afterwards, a branch-and-cut algorithm is proposed by exploring different branching strategies and tailored separation algorithms. Computational results based on real-case instances from an automobile manufacturing company demonstrate that the costs are considerably reduced by considering the multiple visits compared to the single visit of each customer. In addition, our proposed branch-and-cut algorithm outperforms a manual solution method adopted by the company in practice and two solution methods in the literature. Furthermore, two adaptations of our branch-and-cut algorithm also perform better than two state-of-the-art algorithms in solving two closely-related problems in terms of solution quality and computation time. Sensitivity analyses with respect to vehicle capacity, vehicle duration, and vehicle speed are conducted to derive several valuable managerial insights.

Comparative Ranking Preferences Decision Analysis through a Novel Fuzzy TOPSIS Technique for Vehicle Selection

Acquiring a vehicle or financing its purchase is often considered a luxury, particularly for middle-class households. In addition to their primary concern, consumers prioritize distinguishing elements such as vehicle type, size, capacity, engine power, fuel efficiency, safety features, and life-cycle costs. Furthermore, the choice of vehicle has multiple factors and is contingent upon the financial prosperity of the household, allowing for precise expression. The decision-making process is complex and entails selecting the most appropriate alternatives. This study proposes a novel fuzzy technique for order of preference by similarity to the ideal solution (FTOPSIS) method to solve vehicle selection multi-criteria decision-making (MCDM) problems. Decision-makers express their opinions on each alternative and criterion in linguistic terms, in terms of generalized interval type-2 trapezoidal fuzzy numbers (GIT2TrFNs). The FTOPSIS process involved utilizing the defuzzification of GIT2TrFNs to acquire the normalization matrix. Finally, the proposed approach prioritizes the options and chooses the best vehicle when faced with conflicting criteria, as illustrated through a numerical illustration.

Review of implementation of rigid pavemant cement clamping work on the reconstruction of the road access to Tol Palaran

The East Kalimantan Provincial Government carried out reconstruction work on the Palaran Toll access road (Palaran Main Stadium). This work is very strategic considering that the route is one of the accesses to national vital objects (obvitnas) of the Samarinda - Balikpapan Toll Road. In addition to providing comfort for road users, the path will be a storefront for Samarinda City. This path will be an access in and out of and to the Palaran Toll Gate. Previously, the two-lane road section was only used for one lane on the right side of the Palaran Main Stadium. The lane was partially destroyed, scrubbed and only became a parking office for trucks and containers. But considering that this road is a provincial road, the existing road conditions are considered unable to accommodate the capacity of vehicles going through this route. So it is necessary to rebuild and improve roads on the East Kalimantan Main Stadium Road Section at Sta. 1+644 – Sta. 3+405 to facilitate economic traffic and also avoid traffic accidents in the area. The methods used to obtain data are direct observation methods of activities that occur in the field and secondary data collection in the form of test result data, and work plan drawings. The purpose of writing this article is to find out the stages of implementation and supervision of cement concrete pavement road work, especially in ironing work. Palaran Toll Access Road Reconstruction Project using iron 32 mm for dowel rods with a length of 450 mm and a distance of 300 mm, iron 16 mm for tie bars with a length of 700 mm and a distance of 750 mm, as well as iron 12 mm for dowel fulcrum
 reinforcement and tie bar.

Pendekatan Machine Learning untuk Analisis dan Visualisasi Data Jembatan Timbang

In this research, a machine learning approach, especially a decision tree model, is implemented to improve the analysis and visualization of weighbridge data in Indonesia. The evaluation results show that the decision tree model provides better insight in predicting the carrying capacity, dimensions and loading procedures of vehicles. The advantage of this model lies in its combination of low Mean Squared Error (MSE) and high R-squared, indicating its effectiveness in capturing data variance and providing accurate predictions. The use of decision tree models can be a valuable tool in improving the visualization of bridge weighing data, allowing users to gain additional insights and understand the complex dynamics within the data. In addition, the model's adaptability to various types of data makes it a versatile analysis tool. The positive implications of using this model open up opportunities to understand more deeply the logic of predictions and make more informed decisions. As a suggestion, increasing the number and quality of weighing equipment, wider application of information and communication technology, human resource training, and cross-sector collaboration can further strengthen weighbridge management in Indonesia.

Multi-Objective Planning of Commuter Carpooling under Time-Varying Road Network

Aiming at the problem of urban traffic congestion in morning and evening rush hours, taking commuter carpool path planning as the research object, the spatial correlation of traffic flow at adjacent intersections is mined using convolutional neural networks (CNN), and the temporal features of traffic flow are mined using long short-term memory (LSTM) model. The extracted temporal and spatial features are fused to achieve short-term prediction. Considering the travel willingness of drivers and passengers, a multi-objective optimization model with minimum driver and passenger loss time and total travel time is established under the constraints of vehicle capacity, time windows and detour distances. An Improved Non-dominated Sorted Genetic Algorithm-II (INSGA-II) is proposed to solve it. The open-loop saving algorithm is used to generate an initial population with better quality, and the 2-opt local search strategy is adopted in the mutation operation to improve search efficiency. The influence of vehicle speed on the matching scheme is analyzed. The research results show that under the same demand conditions, the total travel distance of the carpool scheme is reduced by about 56.19% and total travel time is reduced by about 65.52% compared with the non-carpool scheme. Research on carpool matching under time-varying road networks will help with urban commuting efficiency and environmental quality, and play a positive role in alleviating traffic congestion and promoting carpool services.

Heuristics and Metaheuristics for Solving Capacitated Vehicle Routing Problem: An Algorithm Comparison

The Capacitated Vehicle Routing Problem (CVRP) is an optimization problem that involves arranging vehicle routes while considering vehicle capacity. This research aims to compare the effectiveness of several heuristic (Path Cheapest Arc, Path Most Constrained Arc, Savings, Christofides) and metaheuristic (Greedy Descent, Guided Local Search, Simulated Annealing, Tabu Search) algorithms for determining the routing scenarios and vehicle types for faculty transportation between the male campus in Ponorogo and the female campus in Mantingan Ngawi at Universitas Darussalam Gontor. The research involves decision variables for vehicle routing determination and the objective of minimizing the distance traveled. The constraint function includes two options: one vehicle with a capacity of 60 passengers and four vehicles. This research utilizes Google OR Tools with the Python programming language using Google Colab to facilitate the calculation process. The research results indicate that metaheuristic algorithms outperform heuristics for complex case studies (four vehicles). This study recommends using metaheuristic methods, specifically Christofides Guided Local Search and Christofides Simulated Annealing, for determining the best routes with the shortest distance and time. Further research was developed using algorithms such as hyperheuristics or matheuristics.