System Travel Time Research Articles

Recovery Scheduling of Road Networks Considering Day-to-Day Flow Evolution

Natural disasters can lead to substantial disruptions in road networks, making many critical links unusable. It is important to timely repair the damaged links as they allow transportation of emergency services, relief materials, and so forth, after disasters. Many existing studies that focus on optimal recovery of damaged links after disasters assume that only a single agency is available for repair. Moreover, most of the existing studies do not consider travel time on links to be a function of traffic flow passing through the links and assume that the traffic flow gets distributed based on user equilibrium each time a link is repaired. However, such a traffic distribution is unrealistic as it assumes that the traffic flow remains the same across all the days for which a link is repaired and the traffic distribution gets suddenly modified whenever a link is fully repaired. The goal of this paper is to address these gaps in the literature of disaster recovery. We study the problem of determining the optimal repair scheduling of damaged links to minimize the sum of the total system travel time over the repair duration given that multiple repair agencies are available for recovery. Also, we consider a day-to-day traffic flow evolution where the route choices of travelers depend on the travel conditions of the previous day. We formulate this problem as a mixed-integer non-linear program. We proposed two solution methodologies to solve the problem: a genetic algorithm and a greedy algorithm. We tested these methodologies under different settings.

Collaborative strategy for electric vehicle charging scheduling and route planning

AbstractDue to varying energy demands and supply levels in different regions, the distribution of power load exhibits an imbalanced state. It contributes to increased power loss and poses a threat to the security constraints of the electrical grid. Simultaneously, the global energy transition has led to a continuous increase in the proportion of renewable energy integrated into the grid. Electric vehicles (EVs), serving as representative of renewable energy, further magnify this load imbalance with their charging requirements, which poses a significant challenge to the stable operation of the grid. Therefore, to ensure the smooth operation of the grid under the context of renewable energy integration, the authors investigate the coordinated strategies of EV charging scheduling and route planning. The authors first model the coupling of the transportation network with the smart grid as a cyber‐physical system. Subsequently, the authors simulate and analyse the daily charging load curve of the network, capturing the travel characteristics of EVs. Based on this, the authors research the EV charging scheduling in both individual and collective travel scenarios during peak and off‐peak hours. For the off‐peak travel period of EVs, a charging schedule strategy based on travel plans is proposed, which reduces the time cost of EV owners' travel. Furthermore, for the collective travel of a large number of EVs within the system, a multi‐EV charging scheduling strategy based on charging station load balancing is presented. This strategy effectively balances the load levels of various charging stations while reducing the overall system travel time. Ultimately, through experimental results, the authors demonstrate that by deploying appropriate charging scheduling strategies, EVs cease to be a burden on the grid and can be transformed into tools for balancing the loads across different regions.

Managing network congestion with link-based incentives: A surrogate-based optimization approach

Incentive-based travel demand management (IBTDM) programs endow monetary incentives to encourage travel demand redistribution across space and time. They are more appealing than alternatives such as congestion charging because commuters do not need to pay out of pocket. However, such congestion-alleviation solutions are usually managed by small private companies with constrained incentive budgets. Thus, the incentive should be wisely determined so that a limited incentive budget can be effectively used to fulfill maximum social welfare while maintaining the financial health of the IBTDM program. It is essential to know whether IBTDM is financially sound—that is, whether financial investment in IBTDM will lead to more than the equivalent value in total system travel time reduction. However, optimizing the link-based endowment scheme in a large-scale network is challenging because 1) the objective function and the budget constraint are both characterized by expensive-to-evaluate functions without closed form, and 2) it is a large-scale optimization problem that contains massive amount of decision variables. In this study, a computationally efficient surrogate-based optimization framework that is suitable for high-dimensional problems is proposed. A simulation-based dynamic traffic assignment model is used to evaluate the performance of transportation systems, and a Kriging model with partial least squares acts as the surrogate to approximate the simulation model. The results show that the optimal network-wide link-based incentive scheme improves the performance of the system. The higher the incentive budget, the more effective the incentive and the lower the marginal utility of the incentive. Furthermore, in a well-designed incentive scheme, a $1M investment in IBTDM would lead to much more than the equivalent of $1M in total system travel time reduction, which proves the economic viability of IBTDM and provides support for its promotion. IBTDM implemented within smaller regions and tighter incentive budgets produces higher utility ratios.

Designing Post-Disaster Restoration Schemes for Traffic Networks based on Network Topological Indicators

Identifying key road segments and prioritizing their protection to enhance the resilience of road networks against crisis events remains a pressing issue. In this paper, complex network theory is adopted to determine the importance ranking of road segments and analyse how to select the road segments that need to be rehabilitated in the case of insufficient budget. This study will provide three different methods to assess the importance of road segments, and with the help of user equilibrium model for traffic demand allocation, calculate the system travel time after rehabilitation to evaluate, compare, and analyse different rehabilitation schemes, and then get the rehabilitation scheme with better performance. Meanwhile, it is investigated whether the optimal road segment rehabilitation combination will change under different budget levels. The results of the case study finally show that the rehabilitation set determined by the Clustering Coefficient and the betweenness is better, which can provide practical guidance on how to select the prioritized rehabilitation segments.

Day-to-day traffic control for networks mixed with regular human-piloted and connected autonomous vehicles

The emerging connected autonomous vehicle (CAV) technology offers a significant opportunity to address the congestion problem caused by the selfish routing behavior of human drivers. If the transportation management center (TMC) can control the routing of all vehicles on the network, they could easily reverse this effect. However, during the transition period, CAVs will share roads with human-piloted vehicles (HVs). In the era of mixed autonomy, a more realistic scenario is to route the CAVs to lead the traffic state towards the desired equilibrium. We first consider a repeated routing game in which the TMC can control the routes of CAVs to minimize the system travel time, i.e., CAVs follow the system optimal (SO) routing principle. In this manner, we can influence the travel time traversing each link, which indirectly influences HV users’ route choices. As a consequence, HV users may adjust their routes dynamically through day-to-day (DTD) learning to minimize individual travel cost. On the other hand, the SO routing strategy may induce higher travel time for CAVs than HVs within the same origin–destination (OD) pair. To address such inherent unfairness, road pricing is used to influence route choices of HVs to achieve specific supply regulation targets, e.g., traffic restraint. We present qualitative analysis on the stability of the controlled DTD dynamics and the effect of road pricing regarding the unfairness. To the best of our knowledge, this paper is one of the first to design dynamic pricing schemes for a mixed autonomy system under DTD learning dynamics. We carry out numerical experiments to demonstrate the effectiveness of the control schemes. The cooperative CAVs could reduce the total travel time when the market penetration rate of CAVs reaches the lower minimum control ratio that the network could escape the user equilibrium (UE) condition. When the market penetration rate of CAVs reaches the upper minimum control ratio, the network would achieve SO condition even though there are non-cooperative HVs. On the other hand, the pricing scheme can reduce the unfairness index and total travel time of the network when the market penetration rate of CAVs is low.

Capital-constrained maintenance scheduling for road networks considering traffic dynamics

Road network maintenance scheduling mainly considers budget limits in the previous studies and largely ignores the capital constraints. This study proposes a bi-objective mixed integer programming model, in which the net present value (NPV) is maximized and the increased total system travel time (ITSTT) due to maintenance activities under the capital constraints is minimized. Since the road network flows cannot reach an equilibrium state overnight due to the variation of network capacity, a link-based day-to-day dynamics model is developed to simulate the transient fluctuation in the traffic flows and calculate the total system travel time of each day. The bi-objective model is solved by the nondominated sorting genetic algorithm-II (NSGA-II) that generates a set of optimal Pareto solutions. The TOPSIS method is then adopted to determine the best compromise solution. Finally, a case study is conducted to demonstrate the effects of key parameters on the values of the two objectives.

A Battery Electric Vehicle Transportation Network Design Model with Bounded Rational Travelers

With governments worldwide emphasizing environmental protection and the global focus on carbon reduction, the battery electric vehicle (BEV) industry has developed rapidly. An urban transportation network with BEVs as the main form of transportation will soon become mainstream. Motivated by the abovementioned background, a BEV transportation network design problem is investigated, and a network design model is established. The model aims to minimize the system travel time of BEV transportation networks and optimize the government’s lane expansion scheme (the location and number of lanes) under a limited budget. To consider the travel characteristics of BEV drivers, the charging time, range anxiety, and bounded rationality factors are simultaneously incorporated into the model. A heuristic algorithm is designed based on the active set algorithm to obtain the local optimal solution to the actual-scale problem. Moreover, a cutting-plane method is used to convert the original problem into a different form, and a column generation technique is embedded in the abovementioned algorithm to avoid the enumeration of paths. Sensitivity analyses of different levels of rationality of BEV drivers and government investment scales are performed. The experimental results demonstrate that the model and algorithm can effectively solve the problem and provide decision support for the government in formulating transportation infrastructure construction policies.

Traffic assignment in urban transportation network problem with emission constraints in China: a cooperative game theory

Traffic assignment in urban transport planning is the process of allocating traffic flows in a network. Traditionally, traffic assignment can reduce travel time or travel costs. As the number of vehicles increases and congestion causes increased emissions, environmental issues in transportation are gaining more and more attention. The main objective of this study is to address the issue of traffic assignment in urban transport networks under an abatement rate constraint. A traffic assignment model based on cooperative game theory is proposed. The influence of vehicle emissions is incorporated into the model. The framework consists of two parts. First, the performance model predicts travel time based on the Wardrop traffic equilibrium principle, which reflects the system travel time. No travelers can experience a lower travel time by unilaterally changing their path. Second, the cooperative game model gives link importance ranking based on the Shapley value, which measures the average marginal utility contribution of links of the network to all possible link coalitions that include the link, and assigns traffic flow based on the average marginal utility contribution of a link with system vehicle emission reduction constraints. The proposed model shows that traffic assignment with emission reduction constraints allows more vehicles in the network with an emission reduction rate of 20% than traditional models.

A day-to-day dynamic model for mixed traffic flow of autonomous vehicles and inertial human-driven vehicles

Autonomous vehicle (AV) technology is likely to bring unprecedented change to existing transport systems due to its potential to enhance safety and efficiency. Nevertheless, before entering a fully AV era, human-driven vehicles (HVs) and AVs are expected to coexist for a long time, during which HVs and AVs could behave differently in route choice. On the one hand, compared with HVs, AVs can acquire more comprehensive traffic information and also process the information more swiftly. On the other hand, HV users may habitually maintain a satisfactory path but not necessarily choose the optimal one to the same destination to save cognitive during daily trips. AV users, however, may not show such behavior on account of the advantages of AVs. To capture these features in the coexistent period, understand their effects on the evolution of daily traffic flow, and provide managerial insights, this study presents a deterministic discrete time day-to-day dynamic assignment model for the mixed HV and AV traffic flows. In which both AV users and HV users choose their path according to their perception, AV users have better perceptual ability than HV users, and bounded behavioral inertia is first introduced to depict the travel behavior that HV users habitually maintain a satisfactory path during daily trips. The existence and uniqueness of the fixed-point and the convergence conditions of the proposed dynamic model are demonstrated and given. Theoretical results are verified in a simple two-path network, the Nguyen-Dupuis network, and the Sioux Falls network, respectively. The interactions between AVs and HVs are explored in the three numerical examples. Our research indicates that the introduction of AVs can reduce the overall travel time of a transport system and increase road capacity. Bounded behavioral inertia of HV users can maintain more HV flows on a better route, brings a more balanced distribution of travel flow on different paths, and facilitate the stability of a whole transport system.

Analysis of the location problem of emergency attendance teams for a railroad through optimization

AbstractRail operators worldwide are seeing an increase in demand for services, driven by expanding global trade and the use of export commodities. Punctual service in a train stopped on the railroad is extremely important since the delay in service significantly impacts performance, asset utilization, and railroad productivity. The present work addresses the development and the solution of an optimization model for locating railway service teams that serve locomotives that need emergency maintenance on a Brazilian railway. Additionally, a hypercube model was solved to estimate several performance measures that assist in decision‐making. From the result analysis of different scenarios, it was found that the optimized maintenance teams' location provides superior results to the current ones considering the same resources. Also, it was possible to increase the coverage with double attendance in the studied system, monitoring different indicators such as attendance teams' average system travel time and workload.

Robust path recommendations during public transit disruptions under demand uncertainty

When there are significant service disruptions in public transit systems, passengers usually need guidance to find alternative paths. This paper proposes a path recommendation model to mitigate congestion during public transit disruptions. Passengers with different origins, destinations, and departure times are recommended with different paths such that the system travel time is minimized. We model the path recommendation problem as an optimal flow problem with uncertain demand information. To tackle the lack of analytical formulation of travel times due to capacity constraints, we propose a simulation-based first-order approximation to transform the original problem into a linear program. Uncertainties in demand are modeled using robust optimization to protect the path recommendation strategies against inaccurate estimates. A real-world rail disruption scenario in the Chicago Transit Authority (CTA) system is used as a case study. Results show that even without considering uncertainty, the nominal model can reduce the system travel time by 9.1% (compared to the status quo), and outperforms the benchmark capacity-based path recommendation. The average travel time of passengers in the incident line (i.e., passengers receiving recommendations) is reduced more (−20.6% compared to the status quo). After incorporating the demand uncertainty, the robust model can further reduce system travel times. The best robust model can decrease the average travel time of incident-line passengers by 2.91% compared to the nominal model. The improvement of robust models is more prominent when the actual demand pattern is close to the worst-case demand.

A Trade-off between User-Equilibrium and System Optimal Traffic Assignments for Congestion Mitigation in City Networks

Major cities worldwide are plagued by severe traffic congestion during peak periods. Traffic routing to utilise unused link capacities is a good strategy to reduce congestion and increase network performance. A multi-objective optimization model that seeks a via-media solution between System Optimal (SO) and User Equilibrium (UE) network flows while ensuring better utilization of the excess capacities available in the network is proposed. The number of paths between the O-D pairs is restricted to eliminate paths with long travel times and a constraint on Total System Travel Time (TSTT) is introduced. The proposed problem is solved using the weighted average method to determine Pareto optimal solutions. It is found that restricting the flow to only four paths resulted in a TSTT value that is only marginally higher (1.4%) than the SO model. When evaluated against the UE and SO models, the proposed model significantly improved the link capacity utilization (up to 73%) on some of the links. Additionally, the travel times improved by up to 30% on some paths, compared to the UE model. Although certain paths have longer travel times, the TSTT of Pareto front points is less than or equivalent to the UE model. Thus, the model yields a better distribution of flow in the network and is found to be superior to the existing models. Such a model can potentially contribute to sustainability, safety, and better management of transportation infrastructure.

Modeling Autonomous Vehicles Deployment in a Multilane AV Zone With Mixed Traffic

The advancement of technologies has fast-tracked the deployment of autonomous vehicles (AVs), however, they necessitate the modernization of the existing road infrastructure to meet the technical and safety requirements of AVs. Due to the high cost and prolonged time required for upgrading infrastructure, it is expected that AVs will be introduced gradually into the existing transport network of human-driven vehicles (HVs) either jointly or separately in a special area or zone within that network. In this paper, we propose a mathematical framework for modelling and deployment of multi-lane AV zone in a mixed-user traffic network that takes into account the different route choice decisions of HVs and AVs. The model is used to study two different designs for a multi-lane AV zone, where in Design 1, both lanes are dedicated for AVs inside the AV zone; while in Design 2, one lane is dedicated for AVs, and another lane is for the HV traffic. We then derive an equivalent convex formulation and develop a novel solution method for this complex problem, as well as evaluate and compare the performance of the designs in a realistic large-scale transportation network. Numerical results show that the deployment of a multi-lane AV zone improves network performance in terms of the total system travel time (TSTT). The proposed framework can be used to assist transportation planners and decision-makers to successfully deploy AVs seamlessly with mixed traffic.

Effects of second-best tradable credit scheme on transportation network for travel mobility management

The second-best tradable credit scheme is studied to analyse its effects on travel mobility by a bilevel programming model. The transport authority optimises the credit charging scheme to minimise the total system travel time and travellers choose paths to minimise their generalised travel costs. A genetic algorithm with some improvements on the selection operator is used to solve the proposed bilevel programming model. In numerical experiments, we discuss the implications of travellers’ path choice behaviour and traffic flow distribution under the second-best tradable credit scheme with different combinations of credit-charging links. The results show that the credit charging scheme with all links is obviously better than other schemes in the total system travel time. Besides, the credit charging scheme using congested links or duplicate links of the maximum traffic flow path as credit-charging links is effective in reducing traffic flow on congested links and mitigating the congestion of the whole transportation network.

Analytical and Microsimulation Model Calibration and Validation: Application to Roundabouts under Sight-Restricted Conditions

Analytical models and traffic microsimulation are two widely used platforms for evaluating roundabout operations. The application of the correct inputs and proper specification of calibration parameters should precede the actual simulation, to replicate field traffic conditions. In this sense, simultaneous data collection and estimation of the input, calibration, and validation variables, along with knowledge of their definitions, are crucial. Although simultaneity of data gathering is virtually guaranteed with the use of wide-frame videos captured with an unmanned aerial system (UAS), there are cases where sight distance restrictions may obscure observations of the back of queue and arrival patterns. This paper explores the calibration and validation efforts associated with an analytical platform, SIDRA 9, and a microsimulation model, TransModeler 5, conducted under sight-restricted conditions. Video captured from a drone, followed by trajectory extraction using video processing software, was used to analyze operations on two approaches at a single-lane roundabout. In the process, the team employed a specialized demand estimation method, and developed a novel data collection scheme for estimating the critical headway distribution in TransModeler 5. Because of sight distance constraints, the model validation was limited to the use of the observable system travel time and associated travel speed within the field of view. The comparison results, for both platforms, have confirmed the value of model calibration in more accurately describing field performance. The calibrated models performed differently between the two approaches, with the approach having a larger presence of buses and heavy vehicles yielding slightly poorer results.

A Decentralized and Coordinated Routing Algorithm for Connected and Autonomous Vehicles

Aiming for better mobility and more efficient utilization of transportation networks, emergent connected and autonomous vehicle (CAV) technologies, and the resulting communication capabilities can produce more coordinated and efficient routing behavior. Current routing strategies either rely on a centralized control system which can fail in scaling, or employ decentralized schemes that yield sub-optimal coordination and accordingly poor system performance. This paper presents a Decentralized Collaborative Time-dependent Shortest Path Algorithm ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dec-CTDSP</i> ) with which the CAVs optimize their routes according to the communicated mobility messages from the other CAVs within their connected cluster. These messages are assumed to carry information regarding the vehicles’ location, speed, and preferred path to their destination. We analyzed the impacts of this optimization scheme under various levels of CAV market penetration and communication radius. The results of this study reveal (1) Up to 40% improvement in mean system travel time and speed; (2) Up to 45% increase in travel time and speed prediction reliability; (3) A strong correlation between mean system travel time and network usage distribution; and (4) significant improvements in network utilization as a result of Dec-CTDSP. The performance of Dec-CTDSP, in terms of runtime, convergence, and mobility improvements, is further benchmarked against other random and Dijkstra-based algorithms. The findings of this work will help steer further research on the implementation of coordinated and decentralized multiagent routing optimization in the context of connected and autonomous vehicles.

Distance-based time-dependent optimal ratio control scheme (TORCS) in congested mixed autonomy networks

Connected and automated vehicle technologies provide an opportunity for a more efficient distribution of traffic in urban networks through cooperative routing behavior. Controlling the route choice of a proportion of autonomous vehicles (AVs) to establish the system optimum (SO) condition has already been investigated in static networks. This study aims to develop a time-dependent optimal ratio control scheme (TORCS) in which an optimal ratio of AVs is identified and selected to seek SO routing. The objective of the control scheme is to achieve a reasonable compromise between the system efficiency (i.e., total travel time savings) and the control cost that is proportional to the total distance traveled by SO-seeking AVs. The proposed distance-based TORCS is formulated as a bi-level optimization problem. A mixed equilibrium simulation-based dynamic traffic assignment model (SBDTA) estimates time-dependent mixed traffic patterns in the lower level based on the time-dependent optimal ratios of SO-seeking AVs obtained from the upper level. We also introduce a new concept of frequency control within the TORCS application. The frequency-based TORCS enables the central agent to control the route choice of selected AVs only in the time periods with the most significant impact on system travel time reduction. This is formulated with a binary variable within the TORCS problem indicating an action/no action regarding controlling AVs at a given assignment interval. The applicability of the model is demonstrated on a real large-scale network of Melbourne, Australia.

Stick or carrot for traffic demand management? Evidence from experimental economics

The debate between stick (penalties) and carrot (rewards) strategies has been widely carried out in various academic realms, including transportation. This paper seeks to compare the instantaneous and longitudinal effectiveness of a traditional penalty-based congestion pricing strategy and an innovative Incentive-Based Traffic Demand Management (IBTDM) strategy using an online interactive laboratory experiment focused on departure time choice. We also investigate the social-economic phenomena of loss aversion and risk aversion. The experimental results show that penalties and rewards can both positively induce sustainable travel behavior. The stronger demand-shifting performance of the penalty-based strategy at first demonstrates that ‘loss aversion’ does exist in TDM, at least initially. However, this loss aversion weakens as the experiment continues, which verifies the myopic loss aversion theory. Moreover, risky decision-making in the gambling games proves the existence of risk seeking, which violates the ‘certainty effect’. To overcome the disadvantage of laboratory experiment that participants need to accomplish the experiment all in once for considerably long time, an agent-based model is calibrated based on real participants’ behavior to further verify the long-term effectiveness of penalties and rewards, and to verify the effectiveness of other optimized penalty/reward schemes. The simulated results based on the agent-based model indicate that the theoretical optimal penalty scheme performs better in general in congestion alleviation, while a more reasonable reward scheme that considers psychological factors can achieve the same effectiveness in terms of total system travel time reduction. The findings of this research can be used to guide the design of TDM strategies, and the behavioral data collection approach based on our laboratory experiment is portable for future studies.

Modelling reservation-based shared autonomous vehicle services: A dynamic user equilibrium approach

Shared Autonomous Vehicles (SAVs) are expected to be used for regular and pre-planned trips. Such trips are suitable for reservation-based services, wherein the customer needs to book for a trip in advance. Systems enabling reservation of trips can allow for better planning of routes and schedules, and if optimally designed, enable higher efficiency. The primary objective of this research is to model the effects of such a system, by formulating and solving the combined Dynamic User Equilibrium and Shared autonomous vehicle Chain Formation (DUESCF) problem. The problem is formulated as a bilevel model based on game theory, involving road users and SAV service operator. Given a situation where conventional private and shared autonomous vehicles co-exist, road users select paths and departure times to maximize a perceived utility (commonly treated as minimizing a disutility) by forming a DUE (fixed point problem), and the SAV service operator tries to maximize the performance by forming appropriate SAV chains (combinatorial problem). The final objective of this bilevel model is a traffic assignment that includes SAV chain formation, such that both road users and SAV service operator obtain optimal solutions by reaching a Nash equilibrium, where no player is better off by unilaterally changing their decisions. A solution approach, based on Iterative Optimization and Assignment (IOA) method, is proposed with path flow and SAV performance changes as convergence criteria. Furthermore, the solution approach is tested for its robustness, and a scenario analysis is carried out to evaluate the impacts of reservation-based SAV services. The results show that a ridesharing SAV system is better compared to a carsharing and a mixed system consisting of both, in terms of total system travel time, congestion levels, total vehicle kilometres travelled and vehicle requirements.

AS/R system travel time in class-based storage with different input-output point levels: a proposed formula

PurposeThis paper aims to propose a simulation model integrated with an empirical regression analysis to provide a new mathematical formulation for automated storage and retrieval system (AS/RS) travel time estimation under class-based storage and different input/output (I/O) point vertical levels.Design/methodology/approachA simulation approach is adopted to compute the travel time under different warehouse scenarios. Simulation runs with several I/O point levels and multiple shape factor values.FindingsThe proposed model is extremely precise for both single command (SC) and dual command (DC) cycles and very well fitted for a reliable computation of travel times.Research limitations/implicationsThe proposed mathematical formulation for estimating the AS/RS travel time advances widely applied methodologies existing in literature. As well as, it provides a practical implication by supporting faster and more accurate travel time computations for both SC and DC cycles. However, the regression analysis is conducted based on simulated data and can be refined by numerical values coming from real warehouses.Originality/valueThis work provides a new simulation model and a refined mathematical equation to estimate AS/RS travel time.