Vehicle Travel Time Research Articles

Solving the combined flexible job shop scheduling and vehicle routing problem with stochastic features

ABSTRACT Today’s competitive market conditions forces companies to implement strategies for the integration of production and transportation activities, but this is studied little in the literature. This paper considers the combined production and transportation problem in which the production system is defined as a flexible jobshop (FJS) and the transportation stage is modelled as a vehicle routing problem (VRP). Stochastic production processing times and vehicle travel times are considered. A simheuristic solution procedure is proposed, as a class of simulation-optimisation approach, based on Ant Colony Optimisation (production stage) and an iterative local search (transportation stage). Randomised data sets are used to evaluate the performance of the proposed solution procedure, with metrics such as total production time and delivery time demonstrating its effectiveness. Experimental outputs show the impact of considering stochasticity of these two parameters for better decision-making. Compared to existing methods, our approach offers significant improvements in efficiency and reliability.

Development of a prioritized traffic light control system for emergency vehicles

This research presents a model for an adaptive traffic signal control system aimed at improving urban traffic regulation. It dynamically adjusts signal timing based on vehicle volume at intersections, prioritizing emergency vehicles by allowing them immediate passage. Utilizing Arduino coding, the system controls traffic light intensity according to the traffic flow, enhancing road safety and efficiency. This innovative approach not only facilitates faster clearance for emergency services without human intervention but also reduces congestion and accident rates. This research creates a model for a prioritized traffic signal control system. When the vehicular volume at the intersection varies, the signal time alters autonomously. It identifies the ambulance/emergency vehicles and allows the green light for emergency vehicles like ambulances, and fire engines. This approach may be used to detect traffic accidents and infractions of automobile spiral motions. When erected on the road, the entire system allows for quick traffic clearing for rescue vehicles without requiring a policeman. The system's design eliminates the need for sensors or radio frequency identification (RFID) tags, simplifying traffic management. Simulations validate that emergency vehicle travel time is significantly reduced, proving the system's effectiveness in streamlining urban traffic flows.

A Hybrid Model Combining Improved Weighted Wolf Optimization and Reinforcement Learning for Estimating Electric Vehicle Travel Time

A Hybrid Model Combining Improved Weighted Wolf Optimization and Reinforcement Learning for Estimating Electric Vehicle Travel Time

Multi-Depot Electric Vehicle–Drone Collaborative-Delivery Routing Optimization with Time-Varying Vehicle Travel Time

This paper presents an electric vehicle-drone (EV–drone) collaborative-delivery routing optimization model that leverages the time-varying characteristics of electric vehicles and drones across multiple distribution centers (i.e., central depots) to address the logistics industry’s low-carbon transformation in the last-mile delivery. The model aims to minimize total delivery costs by formulating a mixed-integer programming (MIP) model that accounts for essential constraints such as nonlinear charging time, time-varying EV travel time, delivery time window, payload capacity, and maximum range. An improved adaptive large-neighborhood search (ALNS) algorithm is developed to solve the model. Experimental results validate the effectiveness of the proposed algorithm and highlight the impact of EV and drone technology parameters, along with the time-varying EV travel times, on the economic efficiency of delivery distribution and route planning.

Asynchronous decentralized traffic signal coordinated control in urban road network

AbstractThis study introduces an asynchronous decentralized coordinated signal control (ADCSC) framework for multi‐agent traffic signal control in the urban road network. The controller at each intersection in the network optimizes its signal control decisions based on a prediction of the future traffic demand as an independent agent. The asynchronous framework decouples the entangled interdependence between decision‐making and state prediction among different agents in decentralized coordinated decision‐making problems, enabling agents to proceed with collaborative decision‐making without waiting for other agents’ decisions. Within the proposed ADCSC framework, each controller dynamically optimizes its signal timing strategy with a unique rolling horizon scheme. The scheme's individualized parameters for each controller are determined based on the vehicle travel time between the adjacent intersections, ensuring that controllers can make informed control decisions with accurate arrival flow information from upstream intersections. The signal optimization problem is formulated as a mixed integer linear program model, which adopts a flexible signal scheme without a fixed phase structure and sequence. Simulation results demonstrate that the proposed ADCSC strategy significantly outperforms the benchmark signal coordination methods in terms of average delay, travel speed, stop numbers, and energy consumption. Experimental analysis on computation time validates the applicability of the proposed optimization model for real‐time implementation. Sensitivity analysis on key parameters in the framework is conducted, offering insights for parameter selection in practice. Furthermore, the ADCSC framework is extended to a road network in Qinzhou City, China, with 45 signalized intersections, demonstrating its effectiveness and scalability in the real‐world road network.

Collaboration and resource sharing in the multidepot time-dependent vehicle routing problem with time windows

Concerns about energy conservation and emission reduction have highlighted the importance of environmentally sound logistics networks in urban areas. These networks are deeply intertwined with urban traffic systems, where variations in transit speeds can significantly increase the energy consumption and carbon emissions of delivery vehicles, compromising the environmental sustainability of urban deliveries. To address this, we propose a multidepot time-dependent vehicle routing problem with time windows, enhancing route planning flexibility and resource configuration. Our approach begins with a route spatiotemporal decomposition method to estimate vehicle travel times and emissions based on varying vehicle speeds. We then develop a multiobjective mixed integer linear programming model that aims to minimize total operating costs, the number of vehicles, and carbon dioxide emissions. A hybrid heuristic algorithm combining spectral clustering, multiobjective ant colony optimization, and variable neighborhood search is proposed to solve the model. This algorithm incorporates collaboration and resource sharing strategies, a pheromone initialization mechanism, a novel heuristic operator that accounts for time dependency, and a self-adaptive update mechanism, enhancing both solution quality and algorithm convergence. We compare the performance of our algorithm with that of the CPLEX solver, multiobjective ant colony optimization, non-dominated sorting genetic algorithm-Ⅲ, and multiobjective particle swarm optimization. The results demonstrate the superior convergence, uniformity, and spread of our proposed algorithm. Furthermore, we apply our model and algorithm to a real-world case in Chongqing, China, analyzing optimized results for different time intervals and vehicle speeds. This study offers robust methodologies for theoretically and practically addressing the multidepot time-dependent vehicle routing problem with time windows, contributing to the development of economical, efficient, collaborative, and sustainable urban logistics networks.

Analysis of Vehicular Travel Time along the Urban Corridor Using Bluetooth Based Data

There has been a tremendous increase in vehicular growth in the last few decades, which has increased congestion in urban areas. Travel time is one of the measures to understand the congestion levels of the urban corridors. The increased use of Bluetooth devices by vehicular drivers may enable the dynamic data collection of travel time through Bluetooth sensors in urban areas. In this context, the objective of the present study is to evaluate the travel time of vehicles using Bluetooth devices and check the reliability of this Bluetooth technology for traffic studies. To fulfil the stated objective, a Bluetooth application was installed on a smartphone in order to collect the vehicular travel time on a selected corridor. A video camera was setup for traffic data collection at a mid-block section, which is used for the validation of the collected travel time data. The K nearest neighbor (KNN) model was developed with travel time (TT) data. The average TT of cars and other vehicles is estimated and found to be higher in the evening compared to the morning. From the developed KNN model, it is also found that the predicted TT for the next time intervals shows that they follow the trend of the actual travel time data obtained from the Bluetooth app. From the results of this study, it is feasible to estimate the travel time in urban areas using Bluetooth devices, which is useful for engineers and planners for suitable policy decision.

A Study of the Improved A* Algorithm Incorporating Road Factors for Path Planning in Off-Road Emergency Rescue Scenarios.

To address the problem of ignoring unpaved roads when planning off-road emergency rescue paths, an improved A* algorithm that incorporates road factors is developed to create an off-road emergency rescue path planning model in this study. To reduce the number of search nodes and improve the efficiency of path searches, the current node is classified according to the angle between the line connecting the node and the target point and the due east direction. Additionally, the search direction is determined in real time through an optimization method to improve the path search efficiency. To identify the path with the shortest travel time suitable for emergency rescue in wilderness scenarios, a heuristic function based on the fusion of road factors and a path planning model for off-road emergency rescue is developed, and the characteristics of existing roads are weighted in the process of path searching to bias the selection process toward unpaved roads with high accessibility. The experiments show that the improved A* algorithm significantly reduces the travel time of off-road vehicles and that path selection is enhanced compared to that with the traditional A* algorithm; moreover, the improved A* algorithm reduces the number of nodes by 16.784% and improves the search efficiency by 27.18% compared with the traditional 16-direction search method. The simulation results indicate that the improved algorithm reduces the travel time of off-road vehicles by 21.298% and improves the search efficiency by 93.901% compared to the traditional A* algorithm, thus greatly enhancing off-road path planning.

Multi-depot routing problem with van-based driverless vehicles

In order to strengthen the coordination between different delivery participants and means of transport, this work proposes one extension of multi-depot routing problems where vans and driverless vehicles are used in combination during the delivery. The operation process mainly includes two parts. One is that, vans carry several driverless vehicles and goods, and drop off or pick up driverless vehicles at stops. Another is that, driverless vehicles departing directly from depots and dropped off by vans deliver goods to customers in cooperation. During the delivery, vans and driverless vehicles are in close cooperation through the proposed multi-depot joint distribution and the proposed van-van joint distribution. By the two modes, one van can depart from one depot and return to another depot, and one driverless vehicle can be set off by one van at one stop and be picked up by another van at another stop. This multi-depot routing problem with van-based driverless vehicles is formulated as a mixed integer programming model which can be solved by a designed heuristic algorithm. The sensitivity analyses about the maximum number of driverless vehicles in one van and the maximum traveling time of driverless vehicles are also performed. The results reveal that they have limited effects on the delivery cost and the application of the two modes. In addition, the experimental results demonstrate that the application of the two modes is affected by the distribution of depots and stops.

Analisa Kinerja Jalan terhadap Kecepatan Kendaraan di Ruas Jalan Lingkar Barat Tiga Kenali Besar Kota Jambi

This research aims to identify the main factors that influence vehicle speed. Analyzing the level of service on the West Ring Road 3 in Kenali Besar, Jambi City. Researchers use quantitative methods where data is taken by carrying out direct observations in the field. The objects aimed at by researchers are road geometric data, vehicle volume, vehicle travel time, traffic volume and vehicle speed obtained by conducting surveys and direct measurements in the field. The main factor that influences vehicle operational speed is the number of motorized vehicles and heavy vehicles that exceed road capacity. Where the average number of light vehicles at peak hours is 8,434 and heavy vehicles 1,524. With the average highest speed of motorized vehicles being 30 KM/hour and heavy vehicles 15/14 KM/hour. Level of Service on the West Ring Road Section 3 with a Degree of Saturation value from the direction of Pall 10 - Simpang Rimbo or vice versa from the direction of Simpang Rimbo - Pall 10 has a service value of C (0.45-0.74) in the stable flow zone. Drivers are limited in choosing speed

Collaborative optimization for scheduling manufacturing tasks and transport vehicles considering manufacturer’s time availability in cloud manufacturing

ABSTRACT As an advanced networked intelligent manufacturing model, cloud manufacturing (CMfg) effectively integrates available resources by strengthening the connection among various manufacturers to solve complex manufacturing problems. Due to the geographical dispersion of manufacturers and the cooperativity nature of the manufacturing process, logistic impacts are non-negligible in scheduling. This paper focuses on the collaborative scheduling of manufacturing and transportation services. To make full use of the manufacturer’s capacity without affecting his benefit, local tasks are scheduled with higher priority on a manufacturer while the service time for CMfg tasks is assumed to be fragmented and represented as a set of available time windows. Based on these considerations, a collaborative optimization model for CMfg on task and vehicle scheduling with manufacturing service windows is established, in which the completion time of all CMfg orders and the total idle traveling time of vehicles are minimized to ensure the efficiency of both manufacturing and logistic service. A dual-loop variable neighborhood search algorithm is designed to solve the problem. Comprehensive experiments are conducted, showing improvements of 2.41% to 29.57% in objective and fewer vehicles needed compared with some existing methods, which validate the efficacy of the proposed model and method.

Prediction of Damage and Remaining Life of Yogyakarta-Bawen Toll Road Pavement with Elastic and Viscoelastic Approaches

Toll roads are one of the infrastructure development targets in Indonesia to speed up traffic and vehicle travel times. This paper analyzes the flexible pavement structure of the Yogyakarta-Bawen Toll Road. It predicts damage and residual values using elastic and viscoelastic approaches based on MDPJ Bina Marga 2017 using the KENPAVE Program. The recommended design alternative is pavement with a layer of AC-WC 40 mm, AC-BC 60 mm, Base AC 180 mm, and LPA 300 mm. This alternative is predicted to be able to serve load repetitions in the elastic approach of 432,857,828 ESAL until fatigue cracking occurs, 215,176,089 ESAL until rutting occurs, and 136,575,580 ESAL until permanent deformation occurs. Analysis using the viscoelastic approach can provide load repetitions of 153,304,378 ESAL until fatigue cracking occurs, 47,014,109 ESAL until rutting occurs, and 71,859,938 ESAL until permanent deformation occurs. Based on the elastic approach, the pavement's service life is 33 years, with initial damage in the form of permanent deformation. In comparison, the viscoelastic approach has a service life of 22.7 years, with initial damage in the form of rutting.

Deep Reinforcement Learning Technique for Traffic Metering in Connected Urban Street Networks

Proper metering can improve traffic operations in congested urban street networks. The available approaches either (a) use macroscopic fundamental diagrams to model traffic dynamics or (b) use numerical time–space discretization of the hydrodynamic traffic flow model with high computational requirements. Therefore, they either (a) do not represent traffic dynamics accurately or (b) are not suitable for online applications. This study introduces a deep reinforcement learning (DRL) methodology to capture traffic dynamics on a micro-level scale with the capability of capturing detailed traffic dynamics with low computational time. The DRL methodology employs two neural networks that map the location of connected vehicles in a network to traffic metering signal indications and estimate the objective function of the traffic metering problem. The outputs of the neural networks are used to construct a loss function, whose optimization provides the optimal parameters for the neural networks. Because of the complexity of the loss function, the gradient descent optimization technique with Monte Carlo simulation is used to optimize the loss function. The proposed methodology was tested on a simulated case study network in Vissim software with 20 intersections. The numerical results showed that the methodology increased throughput by 41.2% and 21.3% and reduced the total travel time of vehicles by 3.4% and 15.5% compared to a no-metering strategy. Comparing the computational time of the proposed methodology with one of the existing traffic metering approaches also showed the potential of the methodology for online applications.

Analysis of the Covid-19 pandemic on preferences of transport modes

The Covid-19 pandemic has affected the entire world and changed many aspects of daily life, including transportation behaviour and preferences. This study examines the situation of transportation behaviour and preferences before and after the Covid-19 pandemic process through a survey study. A total of 471 people participated in the survey, and 30 questions were posed to participants regarding transportation modes preference, daily usage habits, public transportation usage rate, income status, perceived risk level of transportation systems, preferences before and after the pandemic process, and more. The answers were classified, evaluated and statistically analysed. The results showed that users were significantly affected by the Covid-19 process and changed their transportation mode preferences. The use of buses in urban journeys decreased, and the use of private vehicles significantly increased after the Covid-19 process. Hygiene was the key factor in travel, followed by the vehicle occupancy rate, ventilation, fare and travel time factors. The perceived risk levels of public transportation systems were ranked as metrobus, minibus (paratransit), bus, metro, tram and ferry. The study suggests that future transportation system designs should consider user preferences, as well as changes in education and working conditions, and pandemic conditions.

Simulation Approach to Estimate the Potential of High-Occupancy Toll Lanes on German Freeways

This paper focuses on the potential of HOT lanes (high-occupancy toll lanes) in Germany. For this, various scenarios were defined and simulated on a synthetic road network that adheres to German guidelines. The results demonstrated that HOT lanes offered travel time advantages for carpoolers and HOT vehicles but led to longer travel times for single-occupancy vehicles and trucks. Scenarios with higher costs per kilometer generally reinforced the promotion of high-occupancy vehicles and encouraged a shift toward public transport. In contrast, low cost rates had a negative impact on the desired modal split, as the low cost of using the HOT lane became an incentive for comfortable HOT travel rather than carpooling or using public transport. With regard to CO2 and NOx emissions, we demonstrated the unexpected consequences of HOT lanes, when the general traffic flow worsened. In all scenarios, the emissions for both groups were consistently higher than in the baseline scenario without a managed lane, because of the reduced traffic flow. Overall, scenarios with higher costs per kilometer were more favorable in relation to emissions, as the displacement effect toward carpooling vehicles and public transport reduced the overall number of vehicles in the system. This issue may need to be addressed with the implementation of intelligent traffic management systems, especially those that can influence speed. Taking a comprehensive view of the results, HOT lanes in Germany offer the potential for improving traffic conditions and optimizing modal split. However, the limitations of their application must be taken into account.

Predicting vehicle travel time on city streets for trip preplanning and predicting heavy traffic for proactive control of street congestion

We investigate if the vehicle travel time after 6 h on a given street can be predicted, provided the hourly vehicle travel time on the street in the last 19 h. Likewise, we examine if the traffic status (i.e., low, mild, or high) after 6 h on a given street can be predicted, provided the hourly traffic status of the street in the last 19 h. To pursue our objectives, we exploited historical hourly traffic data from Google Maps for a main street in the capital city of Jordan, Amman. We employ several machine learning algorithms to construct our predictive models: neural networks, gradient boosting, support vector machines, AdaBoost, and nearest neighbors. Our experimental results confirm our investigations positively, such that our models have an accuracy of around 98–99% in predicting vehicle travel time and traffic status on our study’s street for the target hour (i.e., after 6 h from a specific point in time). Moreover, given our time series traffic data and our constructed predictive models, we inspect the most critical indicators of street traffic status and vehicle travel time after 6 h on our study’s street. However, as we elaborate in the article, our predictive models do not agree on the degree of importance of our data features.

Simulation Modeling and Optimization of Traffic Flows in Local Sections of the Street Road Network Using AnyLogic

An approach to simulation modeling and optimization of traffic flows in local sections of the street road network using AnyLogic and heuristic algorithms is presented. A feature of this approach is the use of agent-based (ABM) and discrete-event simulation methods supported in the AnyLogic system. An original swarm optimization algorithm (PSO) is proposed, modified for the problem of optimizing road traffic on a real section of the road (near the Yugo-Zapadnaya metro station in Moscow). Numerical experiments were performed to confirm the possibility of optimizing the duration of traffic light phases in order to minimize the average travel time of vehicles on a simulated section of the street road network.

Geographic Accessibility and Completion of Initial Low-Dose CT-Based Lung Cancer Screening in an Urban Safety-Net Population.

Recent modifications to low-dose CT (LDCT)-based lung cancer screening guidelines increase the number of eligible individuals, particularly among racial and ethnic minorities. Because these populations disproportionately live in metropolitan areas, we analyzed the association between travel time and initial LDCT completion within an integrated, urban safety-net health care system. Using Esri's StreetMap Premium, OpenStreetMap, and the r5r package in R, we determined projected private vehicle and public transportation travel times between patient residence and the screening facility for LDCT ordered in March 2017 through December 2022 at Parkland Memorial Hospital in Dallas, Texas. We characterized associations between travel time and LDCT completion in univariable and multivariable analyses. We tested these associations in a simulation of 10,000 permutations of private vehicle and public transportation distribution. A total of 2,287 patients were included in the analysis, of whom 1,553 (68%) completed the initial ordered LDCT. Mean age was 63 years, and 73% were underrepresented minorities. Median travel time from patient residence to the LDCT screening facility was 17 minutes by private vehicle and 67 minutes by public transportation. There was a small difference in travel time to the LDCT screening facility by public transportation for patients who completed LDCT versus those who did not (67 vs 66 min, respectively; P=.04) but no difference in travel time by private vehicle for these patients (17 min for both; P=.67). In multivariable analysis, LDCT completion was not associated with projected travel time to the LDCT facility by private vehicle (odds ratio, 1.01; 95% CI, 0.82-1.25) or public transportation (odds ratio, 1.14; 95% CI, 0.89-1.44). Similar results were noted across travel-type permutations. Black individuals were 29% less likely to complete LDCT screening compared with White individuals. In an urban population comprising predominantly underrepresented minorities, projected travel time is not associated with initial LDCT completion in an integrated health care system. Other reasons for differences in LDCT completion warrant investigation.

Multiagent Q-Learning Approach for the Recharging Scheduling of Electric Automated Guided Vehicles in Container Terminals

In recent years, advancements in battery technology have led to increased adoption of electric automated guided vehicles in container terminals. Given how critical these vehicles are to terminal operations, this trend requires efficient recharging scheduling for automated guided vehicles, and the main challenges arise from limited charging station capacity and tight vehicle schedules. Motivated by the dynamic nature of the problem, the recharging scheduling problem for an entire vehicle fleet given capacitated stations is formulated as a Markov decision process model. Then, it is solved using a multiagent Q-learning (MAQL) approach to produce a recharging schedule that minimizes the delay of jobs. Numerical experiments show that under a stochastic environment in terms of vehicle travel time, MAQL enables the exploration of better scheduling by coordinating across the entire vehicle fleet and charging facilities and outperforms various benchmark approaches, with an additional improvement of 18.8% on average over the best rule-based heuristic and 5.4% over the predetermined approach. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72101203], the Shaanxi Provincial Key R&D Program, China [Grant 2022KW-02], and the Singapore Maritime Institute [Grant SMI-2017-SP-002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0113 .

Spatiotemporal differentiation calendar for car and truck flow on expressways: A case study of Jiangsu, China

The transport objects and demands of cars and trucks differ, leading to different spatiotemporal characteristics in their flow patterns. Existing studies on expressway flow are mainly focused on the overall traffic, however, a decomposition analysis of cars and trucks is necessary to accurately explore traffic prediction, management, and dynamic toll policy. This study presents calendars and spatial distribution of car and truck flow based on toll fee data in Jiangsu Province, China. Additionally, the influencing factors are explored by using Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. The results show that: (1) car flow follows a regular pattern of low from Monday–Thursday and high from Friday–Sunday, while high from Monday–Friday and low from Saturday–Sunday for truck flow. This law is not obvious in the total flow calendar. (2) The car flow predominantly accumulates in the 0–20 km range, while the truck flow is concentrated within the 20–40 km range. (3) The spatial distributions are stable for both car and truck flow, characterized by higher flows in the southern part of Jiangsu and lower in the northern region. Gaps in truck flow between cities are greater than those in car flow, narrowing during holidays/festivals than on weekdays or weekends. (4) private vehicle possession and travel time to Shanghai are the main influencing factors on car flow during different periods, the number of research institutions and the added value of tertiary industry are positively correlated on weekdays and weekends, respectively. Total retail sales of consumer goods is the main factor affecting truck flow during different periods, total import and export goods can be attributed to weekdays, while per capita disposable income affects weekends and festivals. Finally, the findings of this study offer insights for the optimal implementation of toll policies.