Total Travel Time Research Articles

Traffic impacts of dynamic bus lanes: a simulation experiment of real-world bus operations

As buses often share space with other vehicles, their travel time and reliability are affected by the flow of traffic. This can be mitigated by prioritization measures as bus streets or bus lanes, but such measures are not always possible due to limited space or too large impact on other traffic. An alternative is dynamic bus lanes, which only are reserved for buses when needed. The aim of this article is to explore the feasibility of dynamic bus lanes through a case study of a main arterial towards the city centre of a low/medium sized city with multiple bus lines arriving at different intersections along the main arterial. This is investigated by setting up and calibrating a microscopic traffic simulation model for a real world case. The simulation model is used to study impacts of dynamic bus lanes compared to two cases: current operations of mixed traffic (no bus lane) as baseline, and dedicated bus lane. The simulation results demonstrate that dynamic bus lanes can contribute to keeping a good punctuality even at higher traffic demands, this due to decreased travel time variability. However, the total travel time for all travellers (independent of mode) increase (even if buses are assumed to be full).

Social implications of coexistence of CAVs and human drivers in the context of route choice

Suppose in a stable urban traffic system populated only by human driven vehicles (HDVs), a given proportion (e.g. ) is replaced by a fleet of Connected and Autonomous Vehicles (CAVs), which share information and pursue a collective goal. Suppose these vehicles are centrally coordinated and differ from HDVs only by their collective capacities allowing them to make more efficient routing decisions before the travel on a given day begins. Suppose there is a choice between two routes and every day each driver makes a decision which route to take. Human drivers maximize their utility. CAVs might optimize different goals, such as the total travel time of the fleet. We show that in this plausible futuristic setting, the strategy CAVs are allowed to adopt may result in human drivers either benefitting or being systematically disadvantaged and urban networks becoming more or less optimal. Consequently, some regulatory measures might become indispensable.

Outdoor information panels to convey real-time travel information for transit ridership recovery

Transit agencies in the United States have seen a drastic ridership decline since COVID-19. The research team collaborated with Massachusetts Bay Transportation Authority (MBTA) to utilize Outdoor Information Panels (OIPs) along major highways to deliver real-time travel information (RTTI) to encourage increased transit use. An online interview and a household survey were conducted in sequence to gather Great Boston Area (GBA) travelers’ travel experience, preferred RTTI contents, OIP graphic designs, and their stated mode shift in response to OIPs. The top three information items found to encourage transit use are real-time total travel time, next two train arrivals, and real-time parking availability. Additionally, travel cost is more influential for commuter rail (vs. subway) trips and major event (vs. generic) trips. 79% of transit user participants agree that OIPs would improve their travel experience. Trips with more flexible schedules and/or less requirement on carrying passengers and goods, such as social/recreational and major-event trips, are more influenced by RTTI than work, family, and shopping trips. Transit nonusers show a lower tendency to increase their transit use compared to transit users, with their potential increases varying more by trip purpose. A 2.1% emission reduction from work trips is estimated using a regional travel demand model for the GBA. Frequent transit users and nonusers (car users) contribute significantly to emission reductions. Frequent transit users contribute due to their substantial increase in transit use per person, despite being a smaller proportion of the traveling population, while nonusers contribute due to their large proportion, despite a smaller per-person increase in transit use.

Optimization and Implementation Framework for Connected Demand Responsive Transit (DRT) Considering Punctuality

Demand Responsive Transit (DRT) is gaining attention as a flexible and efficient solution for connecting urban transit hubs, but challenges such as travel time variability and punctuality remain significant barriers. This study develops a robust optimization framework with variable travel speed to address these issues, minimizing user and operator costs while reducing transfer waiting times. The framework incorporates variable travel speeds and employs a genetic algorithm to optimize routes and operations compared to many studies using constant commercial speed. Experiments conducted in Hwaseong, South Korea, analyzed scenarios with varying service rates, vehicle capacities, and detour ratios. Results show that implementing punctuality-constrained DRT reduces total travel times by 14% compared to subways and 36% compared to buses, highlighting its potential to significantly improve user convenience and operational efficiency. The findings suggest that carefully designed DRT systems with highly reliable punctuality can enhance urban mobility by integrating seamlessly with existing transit networks, providing a cost-effective and reliable alternative to traditional public transport.

Enhancing High-Speed Railway Timetable Resilience: A Two-Level Spatiotemporal Network Model Focused on Disturbance Absorption

Enhancing the resilience of train timetables can effectively improve their resistance and recovery capabilities against operational disturbances, thereby ensuring the stable operation of the railway system and the quality of passenger transportation services. This paper defined timetable resilience as three parts: disturbance absorptive capacity, resistance capacity, and post-disturbance recovery capacity. These capacities were quantitatively evaluated using the buffer time of trains, the number of train delays at departures and arrivals, and the duration of delay state. The evaluation metrics of the three resilience capacities and the total travel time of trains were used as the objective to establish a spatiotemporal network model. Utilizing actual operational data from the Beijing–Shanghai high-speed railway in China, the model was validated through a case study. Sensitivity analysis of the model's key parameters was conducted under two experimental scenarios: routine operational disturbances and speed restrictions on specific sections. Results showed that our model can effectively reduce the delay time and the number of delays in both the routine operational disturbance scenario and the 300 km/h speed restriction scenario with a frequent disturbance value of 2 min. Moreover, the model's performance with 3-min frequent disturbance outperformed that with 2-min frequent disturbance in speed restriction at 250 km/h and 200 km/h, yielding a 30% improvement.

Effects of Anthropomorphic Driving Vehicles on Traffic Flow

Autonomous driving has many positive impacts, such as improving driver and passenger safety, comfort, and traffic efficiency, but all these advantages are based on people’s trust and acceptance of this mode of driving. Anthropomorphic driving can enhance the trust and comfort of drivers and passengers and is seen as a feasible measure to increase people’s acceptance of autonomous driving. This paper reports the microscopic traffic simulation of three scenarios around a frequently congested intersection, using non-automated vehicles, autonomous driving vehicles, and anthropomorphic driving vehicles to explore their impact on traffic efficiency. The result shows that, compared to non-automated vehicles, both autonomous vehicles and anthropomorphic driving vehicles can improve traffic efficiency during congested periods, increase traffic volume per unit time, reduce the total travel time and time loss, and have a higher average speed. Compared to autonomous vehicles, anthropomorphic driving has a shorter total travel time and a similar time loss. In terms of average speed, anthropomorphic driving performed better than autonomous driving in terms of both congested and non-congested times. In summary, compared to non-automated vehicles, autonomous driving vehicles have a positive effect on improving traffic efficiency, while, compared to autonomous driving, anthropomorphic driving has more advantages in increasing traffic efficiency and reducing traffic congestion.

Optimal Lane Allocation Strategy in Toll Stations for Mixed Human-Driven and Autonomous Vehicles

Highway toll stations are equipped with electronic toll collection (ETC) lanes and manual toll collection (MTC) lanes. It is anticipated that connected autonomous vehicles (CAVs), MTC human-driven vehicles (MTC-HVs), and ETC human-driven vehicles (ETC-HVs) will coexist for a long time, sharing toll station infrastructure. To fully leverage the congestion reduction potential of ETC, this paper addresses the problem of ETC lane allocation at toll stations under heterogeneous traffic flows, modeling it as a mixed-integer nonlinear bilevel programming problem (MINLBP). The objective is to minimize total toll station travel time by optimizing the number of ETC lanes at station entrances and exits while considering ETC-HVs’ lane selection behavior based on the user equilibrium principle. As both upper-level and lower-level problems are convex, the bilevel problem is transformed into an equivalent single-level optimization using the Karush–Kuhn–Tucker (KKT) conditions of the lower-level problem, and numerical solutions are obtained using the commercial solver Gurobi. Based on surveillance video data from the Liulin toll station (Lianhuo Expressway) in Zhengzhou, China, numerical experiments were conducted. The results illustrate that the proposed method reduces total vehicle travel time by 90.44% compared to the current lane allocation scheme or the proportional lane allocation method. Increasing the proportion of CAVs or ETC-HVs helps accommodate high traffic demand. Dynamically adjusting lane allocation in response to variations in traffic arrival rates is proven to be a more effective supply strategy than static allocation. Moreover, regarding the interesting conclusion that all ETC-HVs choose the ETC lanes, we derived the relaxed analytical solution of MINLBP using a parameter iteration method. The analytical solution confirmed the validity of the numerical experiment results. The findings of this study can effectively and conveniently guide lane allocation at highway toll stations to improve traffic efficiency.

Efficiency of energy-consuming random walkers: Variability in energy helps.

Energy considerations can significantly affect the behavior of a population of energy-consuming agents with limited energy budgets, for instance, in the movement process of people in a city. We consider a population of interacting agents with an initial energy budget walking on a graph according to an exploration and return (to home) strategy that is based on the current energy of the person. Each move reduces the available energy depending on the flow of movements and the strength of interactions, and the movement ends when an agent returns home with a negative energy. We observe that a uniform distribution of initial energy budgets results in a larger number of visited sites per consumed energy (efficiency) compared to case that all agents have the same initial energy if return to home is relevant from the beginning of the process. The uniform energy distribution also reduces the amount of uncertainties in the total travel times (entropy production) which is more pronounced when the strength of interactions and exploration play the relevant role in the movement process. That is, variability in the energies can help to increase the efficiency and reduce the entropy production especially in presence of strong interactions.

Travel Time Variability and Spatio-Temporal Analysis of Urban Streets Using Global Positioning System: A Review

Travel Time estimation is largely caused by the stochastic process of arrivals and departures of vehicles and its reliability measurements considering important issues for improving operational efficiency and safety for traffic road networks. The exploration of travel time variability and spatio-temporal analysis of urban streets using the Global Positioning System (GPS) concluded that the mixed land uses and travel congestions caused higher travel times and delays. The accessibility indices were increased by increasing access points and decreasing traffic volumes. The Geographic Information System (GIS) networks can produce a model that overcomes some restrictions of accessibility indices. Different prediction models were developed to capture the main parameters related to travel time. It concluded that delay at signalized intersections in terms of stopping delay was the major parameter affecting the total travel time and total delay time of major urban streets. Travel time estimation algorithms based on speed data loop detectors induced insignificant differences when the study route was a relatively short and slow transition from free state to congestion state. Travel time results are affected by the location of sensors and their sparseness, hence estimation errors increase as detector spacing increases.

Business process overhaul in dairy supply chains: An integrated approach of advanced forecasting and vehicle routing techniques

The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.

Topography‐dependent eikonal solver in tilted transverse isotropic media with anelliptical factorization

AbstractIncorporating anisotropy and complex topography is necessary to perform traveltime tomography in complex land environments while being a computational challenge when traveltimes are computed with finite‐difference eikonal solvers. Previous studies have taken this challenge by computing traveltimes in transverse isotropic media involving complex topography with a finite‐difference eikonal equation solver on a curvilinear grid. In this approach, the source singularity, which is a major issue in eikonal solvers, is managed with the elliptical multiplicative factorization method, where the total traveltime field is decomposed into an elliptical base traveltime map, which has a known analytical expression and an unknown perturbation field. However, the group velocity curve can deviate significantly from an ellipse in anellipitically anisotropic media. In this case, the elliptical base traveltime field differs significantly from the anelliptical counterpart, leading to potentially suboptimal traveltime solutions, even though it helps to mitigate the detrimental effects of the source singularity. To overcome this issue, we develop a more accurate topography‐dependent eikonal solver in transverse isotropic media that relies on anelliptical factorization. To achieve this, we first define the coordinate transform from the Cartesian to the curvilinear coordinate system, which provides the necessary framework to implement the topography‐dependent transverse isotropic finite‐difference eikonal solver with arbitrary source and receiver positioning. Then, we develop a semi‐analytical method for the computation of the topography‐dependent anelliptical base traveltime field. Finally, we efficiently solve the resulting quadratic elliptical equation using the fast sweeping method and a quartic anelliptical source term through fixed‐point iteration. We assess the computational efficiency, stability and accuracy of the new eikonal solver against the solver based on elliptical factorization using several transverse isotropic numerical examples. We conclude that this new solver provides a versatile and accurate forward engine for traveltime tomography in complex geological environments such as foothills and thrust belts. It can also be used in marine environments involving complex bathymetry when tomography is applied to redatumed data on the sea bottom.

The Nature and Strategy of Minimizing the Total Travel Time for Long-Distance Driving of an EV

The Nature and Strategy of Minimizing the Total Travel Time for Long-Distance Driving of an EV

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

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

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.