Total Travel Time Research Articles

Research on the Optimization of Urban–Rural Passenger and Postal Integration Operation Scheduling Based on Uncertainty Theory

The integration of postal and passenger transport is an effective measure to enhance the utilization efficiency of passenger and freight transportation resources and to promote the sustainable development of urban–rural transit and logistics. This paper considers the uncertainty in passenger and freight demand as well as transit operation times, constructing an optimization model for integrated urban–rural transit and postal services based on uncertainty theory. Passenger and freight demand, along with the inverse uncertain distribution of events, serve as constraints, while minimizing passenger travel time and the cost for passenger transport companies are the optimization objectives. Taking into account the uncertainty of urban–rural bus travel time, the scheduling model is transformed into a robust form for scenarios involving single and multiple origin stations. The model is solved using an improved NSGA-II (Nondominated Sorting Genetic Algorithm II) to achieve effective coordinated scheduling of both passenger and freight services. Through a case study in Lotus County, Jiangxi Province, vehicle routing plans with varying levels of conservativeness were obtained. Comparing the results from different scenarios, it was found that when the total vehicle operating mileage increased from 1.96% to 62.26%, passenger transport costs rose from 2.95% to 62.66%, while the total passenger travel time decreased from 55.99% to 172.31%. In terms of optimizing costs and improving passenger travel efficiency, operations involving multiple starting stations for a single vehicle demonstrated greater advantages. Meanwhile, at a moderate level of robustness, it was easier to achieve a balance between operational costs and passenger travel time. The research findings provide theoretical support for improving travel conditions and resource utilization in rural areas, which not only helps enhance the operational efficiency of urban–rural transit but also contributes positively to promoting balanced urban–rural sustainable development and narrowing the urban–rural gap.

Adaptive large neighbourhood search for the multi-depot arc routing problem with flexible assignment of end depot and different arc types

This article introduces an advanced solution to optimize street sweeping operations by extending a multi-depot arc routing problem. The key enhancement involves flexible end depot assignments, where vehicles start and conclude shifts at designated depots. A notable constraint requires subsequent shifts to begin from the destination depot of the preceding shift. The problem involves servicing highway exclusively during night shifts, while other arc types can be addressed during both day and night. The objective is to identify optimal shifts meeting practical criteria while adhering to constraints like maximum shift duration. To address this, a mixed-integer linear programming (MILP) model is presented. It aims to minimize the number of shifts and total travel time. Given the computational complexity of large instances, an adaptive large neighbourhood search (ALNS) metaheuristic was developed. This approach incorporates specialized operators that address unique attributes such as arc type and depot assignments, ensuring arcs are repositioned based on their type and proximity to depots. This tailored approach provides a distinct advantage over classical ALNS operators, as numerical tests indicate that the specialized operators are more efficient in comparison. The approach is evaluated on larger and a real-world instances, demonstrating notable performance in solution quality and computational efficiency.

An optimal multi-objective dynamic traffic guidance approach based on dynamic traffic assignment

Based on dynamic traffic assignment (DTA), this paper proposes a system-optimal multi-objective model for dynamic traffic guidance. Firstly, the physical road network is extended into a spatiotemporal network. By considering the impact of traffic congestion, a multi-objective linear programming model is formulated with the objectives of minimizing total travel time, total travel distance, total number of vehicles in the system, and total infrastructure maintenance cost. Subsequently, a solution method is devised, combining multiple objectives into a single composite objective function by weighted summation. Commercial solver like Gurobi is employed for single-objective linear optimization, and we iteratively calculate Pareto-efficient solutions and approximate the Pareto frontier. Ultimately, the precision and efficacy of the proposed model and solution method are validated through real-world travel data. Additionally, detailed analysis of the relationships between different objectives is conducted to reveal their impacts on the overall traffic guidance strategy.

Research on airport express train schedule optimization based on demand-driven air-rail intermodal transportation

The optimization of the train frequency of the Airport express line (AEL) is crucial for improving the efficiency of air-rail intermodal transport. It directly influences passenger transfer convenience and overall service quality, thereby bolstering the competitiveness of the transport system This study focuses on the optimization of “AEL and Flight Succession” in the context of air-rail intermodal transport. By analyzing the departure and landing time of airport flights, we assess the demand from various passenger flows and identify key factors that impact the connection between the AEL and flights. Based on these factors, we develop a demand-driven optimization model for AEL frequency, aimed at minimizing total travel time and the number of unserved passengers. A simulated annealing algorithm is employed to solve this model. The Lanzhou-Zhongchuan AEL serves as a case study for validation. The results demonstrate that the optimized schedule reduces total passenger travel time costs by 0.93% and 3.82%, respectively, while accounting for passenger time sensitivity and fairness principles, with a difference of 2.89% between these scenarios. In addition, the optimization scheme decreases the number of unserved passengers by 14.7% and reduces the percentage of flights and trains failing to meet occupancy constraints by 17%. This study illustrates that the schedule optimization strategy not only effectively increases the number of served passengers but also significantly reduces total intermodal and commuter travel time. Such findings provide a solid scientific foundation for AEL operations and management to develop a more efficient and rational train schedule in the context of air-rail intermodal transport.

Modeling and Analysis of Mixed Traffic Networks with Human-Driven and Autonomous Vehicles

The emergence of connected and automated vehicles (CAV) indicates improved traffic mobility in future traffic transportation systems. This study addresses the research gap in macroscopic traffic modeling of mixed traffic networks where CAV and human-driven vehicles coexist. CAV behavior is explicitly included in the proposed traffic network model, and the vehicle number non-conservation problem is overcome by describing the approaching and departure vehicle number in discrete time. The proposed model is verified in typical CAV cooperation scenarios. The performance of CAV coordination is analyzed in road, intersection and network scenario. Total travel time of the vehicles in the network is proved to be reduced when coordination is applied. Simulation results validate the accuracy of the proposed model and the effectiveness of the proposed algorithm.

Improving Snowplowing Operations in Utah Through Optimization and Visualization

This paper optimizes snowplowing routes across 12 regions in northern Utah, achieving reductions in total travel time, turnaround time, and deadhead miles by averages of 5.04%, 15.01%, and 14.84% respectively. It assesses tradeoffs between operational policies such as echelon vs. non-echelon routing and centralized vs. decentralized optimization, aiding local management decisions. Furthermore, the paper utilizes data visualization to showcase the effectiveness of new routes compared with current practice, providing valuable insights to the Utah Department of Transportation.

Multi-objective optimal trajectory planning for manipulators based on CMOSPBO

Feasible, smooth, and time-jerk optimal trajectory is essential for manipulators utilized in manufacturing process. A novel technique to generate trajectories in the joint space for robotic manipulators based on quintic B-spline and constrained multi-objective student psychology based optimization (CMOSPBO) is proposed in this paper. In order to obtain the optimal trajectories, two objective functions including the total travelling time and the integral of the squared jerk along the whole trajectories are considered. The whole trajectories are interpolated by quintic B-spline and then optimized by CMOSPBO, while taking into account kinematic constraints of velocity, acceleration, and jerk. CMOSPBO mainly includes improved student psychology based optimization, archive management, and an adaptive ε-constraint handling method. Lévy flights and differential mutation are adopted to enhance the global exploration capacity of the improved SPBO. The ε value is varied with iterations and feasible solutions to prevent the premature convergence of CMOSPBO. Solution density estimation corresponding to the solution distribution in decision space and objective space is proposed to increase the diversity of solutions. The experimental results show that CMOSPBO outperforms than SQP, and NSGA-II in terms of the motion efficiency and jerk. The comparison results demonstrate the effectiveness of the proposed method to generate time-jerk optimal and jerk-continuous trajectories for manipulators.

Layered Graph Models for the Electric Vehicle Routing Problem With Nonlinear Charging Functions

AbstractElectric vehicle routing problems (EVRPs) involve the routing of a fleet of electric vehicles (EVs) to visit a set of customers while typically minimizing the total travel and charging time. Due to their limited autonomy, EVs may need to recharge their batteries en‐route at charging stations (CSs). Thus, routing decisions also include which CSs to visit, and how much energy to charge during those visits. These decisions are compounded by the fact that charging times follow a nonlinear charging function with respect to the EV's state of charge (SoC). We propose a layered graph representation for the EVRP with nonlinear charging functions (EVRP‐NL). Specifically, the layers correspond to discretized SoC values. Therefore, the arcs' energy consumption is approximated to match those values. We develop two compact formulations based on the layered graph. Furthermore, we introduced two charging policies that facilitate aligning charging duration with practical considerations. Computational results demonstrate the effectiveness of our formulations. Our best formulation effectively handles instances with up to 40 customers. On those instances, compared to the state‐of‐the‐art compact formulation, our formulation solves 13 more instances to optimality with less than half of the computational time. Considering instances solved by both formulations to optimally, the approximation entailed by our formulation yields a 0.94% deviation on average. Since our best performing formulation is compact, it may be readily used by a broad audience. Furthermore, as the majority of algorithms for the EVPR and its variants are heuristics, our formulation could be beneficial in evaluating the performance of these methods.

Determining Tourist Destination Priorities Using Website-Based Particle Swarm Optimization Methods (Case Study : North Padang Lawas Regency)

. Optimization of tourist routes is very important to minimize travel time and reduce travel costs. This study focuses on optimizing tourist routes in North Padang Lawas Regency using Multi Attribute Utility Theory (MAUT), and Particle Swarm Optimization (PSO) in the context of the Traveling Salesman Problem (TSP). This study discusses problems such as the lack of priority of tourist destinations and the need for shorter travel times. The research process includes problem identification, literature review, data collection through field observations and interviews, and data processing with MAUT to prioritize destinations. The identified priority destinations are Hotel Sapadia Gunung Tua, Barumun Nagari, Batik Sekar Najogi, Durian and Manggis Agrotourism, Rumah Makan Holat Alhamdulillah, Waterboom Gunung Tua, and Candi Bahal I, II, III. Furthermore, PSO is applied to determine the optimal travel route. PSO finds a route with a total travel time of 351 minutes, although the three-day travel time is extended to 375 minutes.

Modal Shift Potential of Different Mode Users Due to Introduction of the First MRT in Dhaka: A Prelaunch Study

Dhaka city is experiencing tremendous growth in traffic. Until recently, the city’s demand for traffic was entirely served by public buses, a mix of motorized and non-motorized paratransit, and private personalized transport. The first ever rail-based metro, mass rapid transit (MRT), namely MRT Line 6, was partially inaugurated on 28 December 2022. Authority expects that there will be visible modal shift. However, MRT systems in many Asian and European countries are attracting much lower private motorists than what is expected. Moreover, in Dhaka, a unique mix in road-based transport with public transit and varieties of paratransit and private personalized vehicles intensifies the uncertainties involved in modal shift. Therefore, based on a field survey done before four (04) months of the partial inauguration of MRT Line 6, this study intends to explore the modal shift potentials of different mode users to a completely new mode and the modal choice factors. The analysis of the results finds overall, 75% of all mode users are willing to shift, while para and public transit users are comparatively more willing than private personalized vehicle users. However, such willingness comes up with one or more conditions: comfort, reasonable fare, reduced total travel time, less crowd, etc. The study finds that affordability, availability, and accessibility factors have an influence on their modal shift behavior. Also, results from binary logistic model identify significant impact of several sociodemographic, trip- and accessibility-related factors influencing modal shift choice. Findings from this study explain the optimism regarding MRT by different mode users and inform the decision-makers about their course of actions including different interventions, strict and carrot approaches to hold on to the potential shifters and attract more.

Solving discrete network design problem using disjunctive constraints

AbstractThis paper introduces a deterministic algorithm to solve the discrete network design problem (DNDP) efficiently. This non‐convex bilevel optimization problem is well‐known as an non deterministic polynomial (NP)‐hard problem in strategic transportation planning. The proposed algorithm optimizes budget allocation for large‐scale network improvements deterministically and with computational efficiency. It integrates disjunctive programming with an improved partial linearized subgradient method to enhance performance without significantly affecting solution quality. We evaluated our algorithm on the mid‐scale Sioux Falls and large‐scale Chicago networks. We assess the proposed algorithm's accuracy by examining the objective function's value, specifically the total travel time within the network. When tested on the mid‐scale Sioux Falls network, the algorithm achieved an average 46% improvement in computational efficiency, compared to the best‐performing method discussed in this paper, albeit with a 4.17% higher total travel time than the most accurate one, as the value of the objective function. In the application to the large‐scale Chicago network, the efficiency improved by an average of 99.48% while the total travel time experienced a 4.34% increase. These findings indicate that the deterministic algorithm proposed in this research improves the computational speed while presenting a limited trade‐off with solution precision. This deterministic approach offers a structured, predictable, and repeatable method for solving DNDP, which can advance transportation planning, particularly for large‐scale network applications where computational efficiency is paramount.

Demand-aware distributed pathfinding for repositioning vehicles in shared-use autonomous mobility services

Shared-use autonomous mobility services (SAMS) have the potential to provide accessible and demand-responsive mobility to passengers, while benefitting from autonomous vehicle (AV) technology and bypassing challenges related to supply-side incentives or individual driver goals. SAMS operators typically aim to achieve efficiency and improved service quality in their fleet operations, both of which are further enabled by the use of AVs. Specifically, fleet repositioning decisions in anticipation of future demand can improve service quality, but existing approaches in the literature seldom consider the problem of routing repositioning vehicles in a way that further improves SAMS objectives. This paper presents an approach for demand-aware distributed pathfinding for repositioning vehicles, which can supplement existing vehicle repositioning approaches. The problem is formulated with a multi-criteria objective that minimizes the vehicles’ total travel time and maximizes their total demand-serving potential, while distributing that potential equitably among the ride-seeking passengers across the transportation network. We evaluate the proposed approach via numerical experiments using an agent-based simulation of SAMS operations in the network of Manhattan in New York City. The proposed approach is compared to a baseline simple shortest path approach for routing the repositioning vehicles. The results demonstrate that mean passenger waiting times for pick-up can be reduced, while also reducing the total vehicle miles and the empty miles travelled due to repositioning. Thus, the proposed approach can help improve the overall system performance in terms of both service quality and efficiency metrics, relative to the baseline approach.

An improved multi-objective method for the selection of driverless taxi site locations

To expedite the large-scale deployment of driverless taxis and advance the autonomous driving industry, research on the location of integrated parking and charging facilities for driverless taxis has emerged as a significant issue in urban traffic. This study employs a progressive “preliminary selection-screening-optimal selection” approach for site selection. First, the preliminary selection of parking sites is conducted by clustering various Points of Interest (POI) types. Subsequently, a multi-objective site selection model is developed to maximize the coverage of demand points, minimize construction costs, address the largest population demands, and minimize the distance between demand points and candidate sites. The improved genetic algorithm NSGA-II is adopted to obtain several Pareto optimal solutions. The evaluation indexes are selected according to operators, users, and the public transport system to estimate the Pareto optimal solutions, and then the final location solution can be obtained. The calculation methods of several key parameters are improved during the modeling process. Location potential and location influence coefficient are selected to adjust the number of driverless taxi parking spaces. Additionally, isochrones drawn based on the actual road network and path planning represent the service range of candidate points. Meanwhile, distance based on actual road network rather than Euclidean distance is introduced to calculate the distance between candidate points. Finally, a case study shows that the method proposed in this study could reduce the total initial travel time to reach the demand points by 64%, which is independent of operational scheduling.

Simulation-Based Optimization for Vertiport Location Selection: A Surrogate Model With Machine Learning Method

We present Vertiport-informed Surrogate-Based Optimization with Machine Learning Surrogates (VinS), a novel framework for solving the vertiport location problem for urban air mobility operations. The primary focus of this work is on the optimization of vertiport locations to facilitate efficient urban air transportation. Our framework helps choose not only the optimal vertiport locations but also the optimal number of vertiports. We develop a simulation model to analyze the impacts of various vertiport location configurations on the efficiency of the transportation network. To expedite the simulation process, a surrogate model is trained using machine learning algorithms, effectively reducing the computational time for evaluating a given vertiport location configuration. With the machine learning surrogate models, we apply a genetic algorithm to find the optimal set of vertiport locations. An empirical study was performed in the San Francisco Bay Area, from which we found that given the optimal set of vertiport locations, we further reduced the total travel time in the entire transportation system in the Bay Area compared with the sampled sets by 0.05% (400 h) on average. We release our framework at: https://github.com/Xuan-1998/LPSim .

Comparison of Djikstra, Hybrid-PSO algorithms for optimizing the distribution route of papaya seeds and honey products (Case Study: PT. Agro Apiari Mandiri)

Dynamic global competencies in the industrial sector drive fierce competition in capturing markets and increasing customer satisfaction, which requires efficiency in various aspects of business including distribution. PT. Agro Apiari Mandiri faces challenges in optimizing delivery routes to avoid delays. This study aims to compare the Dijkstra and Hybrid-PSO algorithms to determine the optimal distribution route in the Bogor, West Java, and Lebak, Banten regions, in order to reduce the distance and delivery time. The research methods include literature study, data collection, and route optimization model creation. The results show that PSO is more efficient in optimizing delivery routes than other methods, with variations in PSO parameters affecting total travel time, number of vehicles, and computing time. Implementation uses hardware and software such as MSI Laptops and Matlab. In conclusion, the use of PSO in distribution route optimization makes a significant contribution to the company's cost and distribution efficiency and can be a reference for further research in distribution route optimization.

A singular, broadly-applicable model for estimating on- and off-path walking travel rates using airborne lidar data

Accurate prediction of walking travel rates is central to wide-ranging applications, including modeling historical travel networks, simulating evacuation from hazards, evaluating military ground troop movements, and assessing risk to wildland firefighters. Most of the existing functions for estimating travel rates have focused on slope as the sole landscape impediment, while some have gone a step further in applying a limited set of multiplicative factors to account for broadly defined surface types (e.g., “on-path” vs. “off-path”). In this study, we introduce the Simulating Travel Rates In Diverse Environments (STRIDE) model, which accurately predicts travel rates using a suite of airborne lidar-derived metrics (slope, vegetation density, and surface roughness) that encompass a continuous spectrum of landscape structure. STRIDE enables the accurate prediction of both on- and off-path travel rates using a single function that can be applied across wide-ranging environmental settings. The model explained more than 80% of the variance in the mean travel rates from three separate field experiments, with an average predictive error less than 16%. We demonstrate the use of STRIDE to map least-cost paths, highlighting its propensity for selecting logically consistent routes and producing more accurate yet considerably greater total travel time estimates than a slope-only model.

Optimization for route selection under the integration of dispatching and control at the railway station: A 0‐1 programming model and a two‐stage solution algorithm

AbstractAt present, the mainstream studies on route selection optimization at the railway station rarely considered the overall punctuality of the operation plans and the seizing route resource between shunting operation and train running, which can endanger the running safety and reduce the efficiency at the station. Therefore, this paper proposes an optimization method for the route selection under the integration of dispatching and control at the railway station. Firstly, the station‐type data structure, the route occupation conflict, and the operation task order were defined. Then, a 0‐1 programming model was constructed to minimize the total delay time and shorten the total travel time of all operations. Finally, a two‐stage solution algorithm based on depth‐first search algorithm and genetic algorithm was designed, and two actual cases of a technical station in China were designed. The instance verification results show that the algorithm can find the satisfactory route scheme in 250 iterations; different delay factors and travel coefficients will get different route schemes, which can provide decision support for dispatchers and operators to select routes. Through comparative analysis of algorithms, it is found that the two‐stage algorithm has higher solving efficiency than the individual depth‐first search algorithm and individual genetic algorithm.

Total capacity and LOS estimation of innovative and conventional roundabouts through macroscopic fundamental diagrams

Traffic congestion remains a significant problem, especially in urban areas. To avoid congestion phenomena, several measures can be adopted, including the construction of roundabouts and the control of the measures of effectiveness (MOEs) at intersections. This article compares different types of roundabouts by estimating the deterministic fundamental diagram (MFD) and using microscopic traffic simulations. The deduction of MFD for different roundabout types (single and double-lane, flower and turbo-roundabout) is based on reasonably simplified traffic and boundary conditions. MFDs are estimated through several OD traffic matrices, considering as outputs the values of the macroscopic traffic variable deduced in time intervals of 5 min and 1 h. The findings from this research, in terms of total capacity, travel times, and level of service (LOS) estimation, may be useful for traffic and highway engineers in many realistic applications to partially face congestion phenomena in urban areas. MFDs allow us to determine the best roundabout type based on the traffic demand and represent a parsimonious method for monitoring and controlling traditional and unconventional or smart roundabouts, even in the presence of connected and automated vehicles (CAVs).

Market Penetration Rate Optimization for Mobility Benefits of Connected Vehicles: A Bayesian Optimization Approach

The advancements of connected vehicle (CV) technologies promise significant safety, mobility, and environmental benefits for future transportation systems. These benefits will largely rely on the market penetration rate (MPR) of CVs and connected infrastructure. However, higher MPR is not guaranteed to result in greater benefits in a transportation system in some cases even if we do not consider the deployment cost of CVs. Therefore, understanding the optimal CV MPR to achieve the best system benefits is informative and can provide some guidance for transportation agencies to use appropriate incentives or other policies to potentially affect the speed of CV adoption. Instead of using the traditional incremental method, this paper proposed a simulation-based approach combined with Bayesian optimization to determine the optimal CV MPR that achieves the highest performance benefits for a freeway segment. The proposed methodology is tested in the I-210 E (in California, U.S.) simulation freeway segment built and calibrated in Simulation of Urban Mobility software as a case study. The weighted sum of the average total travel time on the mainline and the average queue length of on-ramps is formulated as the objective function to optimize the CV MPR. Different weight combinations are tested as different scenarios. The optimization results of these scenarios show that, when the weight of total travel time is high, the optimal CV MPR tends to be high. On the contrary, when the weight of queue length increases, higher CV MPRs may not guarantee higher benefits for the traffic system. The globally optimal CV MPR can be as low as 3%. The case study also confirms the effectiveness of optimizing the CV MPR based on microsimulation and Bayesian optimization.

Vehicle routing problem for cold-chain drug distribution with epidemic spread situation

We investigate a vehicle routing problem considering the influence of epidemic spread (VRP-ES) for the design of a novel cold-chain drug distribution system, in which the disease spread model is used to capture virus transmission characteristics and demand fluctuations. To this end, we aim to minimize the total travel time and transmission risk of the distribution network by incorporating realistic features including priority distribution and temperature control. We propose a hybrid tabu search heuristic (HTS) with a specifically designed initial solution, several neighborhood operators, and diversification strategies to solve this problem. A series of numerical experiments are conducted to test the proposed solution methodology. The virus spread model and vehicle routing results are discussed to analyze the VRP-ES optimization strategies through an empirical case study of Chongqing city in China. Sensitivity analysis is conducted to identify the impact of various parameters on the VRP-ES and provide some management implications as well.