System Travel Time Research Articles

Joint routing and pricing control in congested mixed autonomy networks

Routing controllability of connected and autonomous vehicles (CAVs) has been shown to reduce the adverse effects of selfish routing on the network efficiency. However, the assumption that CAV owners would readily allow themselves to be controlled externally by a central agency for the good of the system is unrealistic. In this paper, we propose a joint routing and pricing control scheme that aims to incentivize CAVs to seek centrally controlled system-optimal (SO) routing by saving on tolls while user equilibrium (UE) seeking human-driven vehicles (HVs) are subject to a congestion charge. The problem is formulated as a bi-level optimization program where the upper level optimizes the dynamic toll rates using the network fundamental diagram (NFD) and the lower level is a mixed equilibrium simulation-based dynamic traffic assignment model (SBDTA) considering different combinations of SO-seeking CAVs. We apply a feedback-based controller to solve for the optimal spatially differentiated distance-based congestion charge from which SO-seeking CAVs are exempt; but UE-seeking HVs are subject to the charge for entering the city center. To capture the distinct microscopic behavior of CAVs in the mixed autonomy traffic, we also implement an adaptive link fundamental diagram (FD) within the SBDTA model. The proposed joint control scheme encourages CAV owners to seek SO routing resulting in less total system travel time. It also discourages UE-seeking HVs from congesting the city center. We demonstrate the performance of the proposed scheme in both a small network and a large-scale network of Melbourne, Australia.

Evaluation of Transit Signal Priority on Arterial Performance - A Comparison between Conventional and Unconventional Intersection Operation

Transit Signal Priority (TSP) shows capability to improve traffic operations through providing transit vehicles a higher priority to cross intersections. However, implementing TSP could impose extra delays to the adjacent traffic. Specifically, TSP imposes extra delay on turning traffic that could be solved by converting the intersections to unconventional intersection. To do so, this paper models assesses and evaluates the possibility for implementing a TSP in arterial roadway in Amman, capital of Jordan, by using real traffic data under different scenarios including conventional and unconventional intersection designs. The evaluation was done using simulation. Simulation results show that the TSP implementation reduces system travel time and improves the overall system delay. Specifically, implementing the combination of TSP with unconventional intersection design reduces corridor travel time, system delay, and queue length. Moreover, simulation results indicated that applying TSP improves individual movements travel time and delay up to 35% per vehicle.

A cordon-based reservation system for urban traffic management

This paper builds a novel framework for the cordon-based road reservation system to manage road traffic in urban areas, and the rules of reserving the right of way are established. Both the simulation experiment and the analytical modeling approaches offer evidence that the reservation system performs better than the basic system and some types of pricing systems. Considering two different types of vehicles (i.e., reserved and unreserved vehicles) and a general link cost function, we conduct three types of experiment simulations, including the basic system, the reservation system, and the pricing system. Although the severity of traffic congestion is mitigated inside the cordon both in the reservation and pricing systems, the reservation system shows the best network performance from the traffic indicators provided by the simulation software. In the analytical modeling approach, the Frank–Wolfe​ algorithm with a modified Bureau of Public Road function is used to analyze the road reservation scheme, and results agree with the expected effectiveness of the reservation both in the aspect of the total system travel time and the equilibrium of the traffic flow in the road network. Finally, practical issues about the application of the road reservation system are discussed.

Information provision and congestion pricing in a risky two-route network with heterogeneous travelers

Travel information and congestion pricing are two major approaches to relieving congestions. This paper examines travelers’ travel strategies and route choices in a road network with a risky route and a safe route, where travel information pricing are jointly implemented. We derive the equilibrium of the risk-averse travelers with heterogeneous Values of Time (VOTs) under various information regimes. We find that information regarding the risky road conditions would not necessarily improve the expected system travel time. When there is no toll, free information surprisingly makes congestion worse. When information is not free, congestion may be reduced if both toll and information are properly priced. We also examine the distributional effects of information provision across travelers with different VOTs. Travelers with low VOTs who do not use information are not affected by the price of information. Travelers with intermediate VOTs always benefit from information provision regardless of its price. Finally, travelers with high VOTs are better off when the information price exceeds a threshold.

Analysis of Macroscopic Traffic Network Impacted by Structural Damage to Bridges from Earthquakes

Highway systems play a key role in providing mobility to society, especially during emergency situations, including earthquakes. Bridges in highway systems are susceptible to damage from earthquakes, causing traffic capacity loss leading to a serious impact on surrounding areas. To better prepare for such scenarios, it is important to estimate capacity loss and traffic disruptions from earthquakes. For this purpose, a traffic-capacity-analysisbased methodology was developed to model the performance of a transportation network immediately following an earthquake using a macroscopic multi-level urban traffic planning simulation model EMME4. This method employs the second order linear approximation (SOLA) traffic assignment and calculates total system travel time for various capacity loss scenarios due to bridge damage from earthquakes. It has been applied to Pohang City in Korea to evaluate the performance of traffic networks in various situations. The results indicate a significant increase in travel time and a decrease in travel speed as the intensity of an earthquake increases. However, the impact on traffic volume varies depending on the bridges. It is assumed that the location of the bridges and traffic routing patterns might be the main reason. The results are expected to help estimate the impact on transportation networks when earthquakes cause traffic capacity loss on bridges.

Multi-objective framework for optimum configuration of human-driven and shared or privately owned autonomous vehicles

Private Autonomous Vehicles (PAV) and Shared Autonomous Vehicles (SAV) are known to improve safety, mobility, roadway capacity, driver productivity and reduce parking costs (due to better utilization of space and self-parking to less expensive spots). However, the increased vehicle miles traveled (VMT), might increase overall emission production, system travel time, and operating costs. Also, the purchase price of autonomous vehicle (AV) is expected to be higher than that of human-driven (conventional) vehicles. A multi-objective mathematical model is proposed to minimize the purchase and operating costs, time spent, and emission production. The proposed model captures the tradeoff between the benefits of increased mobility, reduction in the value of travel of time (VOTT), efficient driving pattern, and the negative impacts of increased VMT, ownership cost due to the adoption of AVs. The proposed framework assists with the development of simplified adoption models that can be used by the policymakers and/or investors. SAVs would be the optimal solution if the replacement rate or CO2 costs are significantly low, or CO costs are sufficiently high. SAVs can also be the optimal solution if the travel time is used efficiently, or the purchase price is below certain relative threshold while minimizing system cost. Considering the private mobility system, PAVs can be the optimal solution only if the on-board amenities are improved, lifetime mileage is increased, AV technology is installed in luxurious cars, and is being adopted by people with high VOTT.

Resilience-Based Optimization of Postdisaster Restoration Strategy for Road Networks

This work proposes a framework for the optimization of postdisaster road network restoration strategies from a perspective of resilience. The network performance is evaluated by the total system travel time (TSTT). After the implementation of a postdisaster restoration schedule, the network flows in a certain period of days are on a disequilibrium state; thus, a link-based day-to-day traffic assignment model is employed to compute TSTT and simulate the traffic evolution. Two indicators are developed to assess the road network resilience, i.e., the resilience of performance loss and the resilience of recovery rapidity. The former is calculated based on TSTT, and the latter is computed according to the restoration makespan. Then, we formulate the restoration optimization problem as a resilience-based bi-objective mixed integer programming model aiming to maximize the network resilience. Due to the NP-hardness of the model, a genetic algorithm is developed to solve the model. Finally, a case study is conducted to demonstrate the effectiveness of the proposed method. The effects of key parameters including the number of work crews, travelers’ sensitivity to travel time, availability of budget, and decision makers’ preference on the values of the two objectives are investigated as well.

Fuzzy modelling of static system optimum traffic assignment problem having multi origin-destination pair

Traffic congestion is an unpreventable problem to avoid in a transportation network and it has negative effects on traffic accident, time wasting, traffic delay and safety problem. Besides, in transportation networks, drivers do not want to deal with traffic jam while traversing between specified origin-destination pair. Therefore, traffic assignment (TA) is imperative to improve traffic management, transportation safety, time, and cost savings. System Optimum Traffic Assignment Problem (SOTAP) is a kind of TA model which aims to minimize the total system travel time on the network, and satisfies the flow conservation constraints. To model the SOTAP more realistically, the imprecise parameters can be taken as fuzzy. Therefore, in this paper, we focus on converting the conventional SOTAP to a fuzzy quadratic programming problem (QPP) which is named System Optimum Fuzzy Traffic Assignment Problem (SOFTAP). Here, link travel time is expressed with BPR function as generally used in the literature by converting to fuzzy except link-dependent parameters. Thus, the nonlinear objective function of SOFTAP is expressed in terms of fuzzy link flows and fuzzy link travel times. A solution approach from the literature is modified to the reconstructed SOFTAP.

Multiclass dynamic system optimum solution for mixed traffic of human-driven and automated vehicles considering physical queues

Dynamic traffic assignment (DTA) is an important method in the long term transportation planning and management processes. However, in most existing system optimum dynamic traffic assignment (SO-DTA), no side constraints are used to describe the dynamic link capacities in a network which is shared by multiple vehicle types. Our motivation is based on the possibility for dynamic system optimum (DSO) to have multiple solutions, which differ in where queues are formed and dissipated in the network. To this end, this paper proposes a novel DSO formulation for the multi-class DTA problem containing both human driven and automated vehicles in single origin-destination networks. The proposed method uses the concept of link based approach to develop a multi-class DTA model that equally distributes the total physical queues over the links while considering explicitly the variations in capacity and backward wave speeds due to class proportions. In the model, the DSO is formulated as an optimization problem considering linear vehicle composition constraints representing the dynamics of the link capacities. Numerical examples are set up to provide some insights into the effects of automated vehicles on the queue distribution as well as the total system travel times.

Optimal decision making in post-hazard bridge recovery strategies for transportation networks after seismic events

In this study, optimal post-hazard bridge recovery strategies were proposed for transportation networks under seismic conditions. To predict the performance of the transportation network, a robust performance measure, total system travel time (TSTT), was employed, and an artificial neural network (ANN)-based surrogate model was developed to enable an accelerated Monte Carlo analysis. In addition, a sensitivity analysis based on the benefit–cost ratio was proposed to support optimal decision making immediately after an earthquake. To demonstrate the proposed methodology, an actual transportation network in South Korea was adopted, and a network map was reconstructed based on geographic information system (GIS) data. A surrogate model for network performance evaluation was constructed using training data generated based on historical earthquake epicenters. In addition, the damage ratio and required recovery days according to the damage states of bridges were employed to perform network recovery analysis. For the numerical analysis, a limited budget was set for each scenario, and the recovery and damage curve were compared with existing priority strategy. The numerical results showed that the priority strategy of bridge restoration determined through the benefit–cost analysis generated a faster recovery curve and significantly reduced the damage, as compared to existing strategy. Therefore, it is concluded that the proposed methodology enables optimal decision making and also helps risk management that can minimize the economic damage.

Convergence behavior for traffic assignment characterization metrics

Traffic assignment is used for infrastructure planning, based on metrics like total system travel time (TSTT), vehicle-miles traveled (VMT) and link or path flows. Algorithms for traffic assignment converge to an equilibrium solution over multiple iterations, but these metrics converge at different rates. Current guidance indicates that freeway link flows stabilize at a relative gap of roughly . This study generalizes this guidance by testing additional networks and metrics, in more experimental settings. Our results reveal that aggregate metrics (VMT and TSTT) stabilize earlier (relative gap ) than link flows (relative gap ), which in turn stabilize slightly before the set of most likely used paths and flows on these paths (relative gap ). These results are stable across the TAPAS and Algorithm B methods for solving assignment. Our results also show strong linear correlations between alternative gap measures, allowing for the translation of stabilization results across other gap definitions as well.

On the Price of Satisficing in Network User Equilibria

When network users are satisficing decision makers, the resulting traffic pattern attains a satisficing user equilibrium, which may deviate from the (perfectly rational) user equilibrium. In a satisficing user equilibrium traffic pattern, the total system travel time can be worse than in the case of the perfectly rational user equilibrium. We show how bad the worst-case satisficing user equilibrium traffic pattern can be compared with the perfectly rational user equilibrium. We call the ratio between the total system travel times of the two traffic patterns the price of satisficing, for which we provide an analytical bound. We compare the analytical bound with numerical bounds for several transportation networks.

Accelerated System-Level Seismic Risk Assessment of Bridge Transportation Networks through Artificial Neural Network-Based Surrogate Model

In this study, an artificial neural network (ANN)-based surrogate model is proposed to evaluate the system-level seismic risk of bridge transportation networks efficiently. To estimate the performance of a network, total system travel time (TSTT) was introduced as a performance index, and an ANN-based surrogate model was incorporated to evaluate a high-dimensional network with probabilistic seismic hazard analysis (PSHA) efficiently. To generate training data, the damage states of bridge components were considered as the input training data, and TSTT was selected as output data. An actual bridge transportation network in South Korea was considered as the target network, and the entire network map was reconstructed based on geographic information system data to demonstrate the proposed method. For numerical analysis, the training data were generated based on epicenter location history. By using the surrogate model, the network performance was estimated for various earthquake magnitudes at the trained epicenter with significantly-reduced computational time cost. In addition, 20 historical epicenters were adopted to confirm the robustness of the epicenter. Therefore, it was concluded that the proposed ANN-based surrogate model could be used as an alternative for efficient system-level seismic risk assessment of high-dimensional bridge transportation networks.

Deep reinforcement learning algorithm for dynamic pricing of express lanes with multiple access locations

This article develops a deep reinforcement learning (Deep-RL) framework for dynamic pricing on managed lanes with multiple access locations and heterogeneity in travelers’ value of time, origin, and destination. This framework relaxes assumptions in the literature by considering multiple origins and destinations, multiple access locations to the managed lane, en route diversion of travelers, partial observability of the sensor readings, and stochastic demand and observations. The problem is formulated as a partially observable Markov decision process (POMDP) and policy gradient methods are used to determine tolls as a function of real-time observations. Tolls are modeled as continuous and stochastic variables and are determined using a feedforward neural network. The method is compared against a feedback control method used for dynamic pricing. We show that Deep-RL is effective in learning toll policies for maximizing revenue, minimizing total system travel time, and other joint weighted objectives, when tested on real-world transportation networks. The Deep-RL toll policies outperform the feedback control heuristic for the revenue maximization objective by generating revenues up to 8.5% higher than the heuristic and for the objective minimizing total system travel time (TSTT) by generating TSTT up to 8.4% lower than the heuristic. We also propose reward shaping methods for the POMDP to overcome the undesired behavior of toll policies, like the jam-and-harvest behavior of revenue-maximizing policies. Additionally, we test transferability of the algorithm trained on one set of inputs for new input distributions and offer recommendations on real-time implementations of Deep-RL algorithms. The source code for our experiments is available online at https://github.com/venktesh22/ExpressLanes_Deep-RL.

Optimal positioning of dynamic wireless charging infrastructure in a road network for battery electric vehicles

Dynamic wireless charging (DWC) offers a plausible solution to extending Battery Electric Vehicle (BEV) driving range. DWC is costly to deploy and thus its locations need to be optimized. This raises a question often encountered in practice for infrastructure investment: how to determine the optimal locations of DWC facilities in a network. In this paper, we propose a sequential two-level planning approach considering the objectives of both the public infrastructure planning agency and the BEV users. Two different planners’ objectives namely, total system travel time and total system net energy consumption are considered. Besides these objectives, constraints such as agency budget, range reassurance, and equity in resource distribution are also addressed at the planner’s level. For each objective, BEV drivers respond by choosing their preferred route based on the location of DWC facilities implemented by the planner. An effective solution algorithm is utilized that has the capability of solving relatively large-scale real-world networks within a reasonable computational time. The numerical experiment and case study results provide useful insights on optimally positioning DWC infrastructure to minimize societal cost and energy.

Travel time model for a tower-based automated parking system

This paper aims to investigate a tower-based automated parking system, with the appearance of a cylinder. The tower crane can simultaneously implement two types of movement: (1) ascending/descending in the vertical direction and (2) rotating counter clockwise/clockwise. The shuttle can move along the diameter to the opposite parking slot. Using the characteristics of the device, we first deduce a travel time model of the system. Then, we obtain the optimal system sizes by solving the model. We further discuss minimising the maximum travel time and the dwelling point of the crane. Finally, sensitivity analyses are also conducted, in order to demonstrate the optimal value of the proposed system. We have varied the height (in time unit) for a fixed area of the system and found that, when the system configuration deviates from the optimal value, the travel time of the parking system increases greatly. We also investigate how and to what degree the different ratio of the parameters affects the expected travel time of a tower-based system. In addition, we investigate the influence of different system configurations (in the light of travel time and footprint) on system performance. The corresponding analysis results can provide the designer the required system size.

Transportation-oriented spatial allocation of land use development: a simulation-based optimization method

Urban development usually deteriorates the transportation system. For sustainable urban development, policymakers often face the challenging problem of how to optimally allocate overall land use quotas across a number of residential locations according to the performance of the transportation system. This is a kind of Stackelberg competition, where policymakers make land use decisions and travelers make behavioral responses. A novel bi-level model is formulated to solve this problem. The upper-level model minimizes the total system travel time by land use allocation, while at the lower level are sequential models with feedback for transportation system equilibrium. The Dirichlet allocation algorithm, a simulation-based heuristic algorithm, is designed to solve this bi-level model. A simulation experiment using the Nguyen–Dupuis network is then used to verify the proposed model and algorithm. The results from the simulation experiment demonstrate that not only are the model and algorithm operational but that they also provide an effective tool for policymakers to plan for land use development.

Development of Arduino UNO Based Automated Storage and Retrieval System

Automated storage and retrieval system (AS/RS) is prototyped to take account of effect of various dwell point strategies and locations of input-output station on expected travel time and throughput of system. The effect of shape function on expected cycle time and throughput of system. Arduino UNO microcontroller is used to control and operate storage and retrieval functions. Arduino UNO software provides complete package of flexible programming platform for control operation of input output devices. Android interfacing to control system provides smarter rand easily accessible human control panel.

A Combined Modal and Route Choice Behavioral Complementarity Equilibrium Model with Users of Vehicles and Electric Bicycles.

The popularity of electric bicycles in China makes them a common transportation mode for people to commute and move around. However, with the increase in traffic volumes for both vehicles and electric bicycles, urban traffic safety and congestion problems are rising due to traffic conflicts between these two modes. To regulate travel behavior, it is essential to analyze the mode choice and route choice behaviors of travelers. This study proposes a combined modal split and multiclass traffic user equilibrium model formulated as a complementarity problem (CP) to simultaneously characterize the mode choice behavior and route choice behavior of both vehicle and electric bicycle users. This model captures the impacts of route travel time and out-of-pocket cost on travelers’ route choice behaviors. Further, modified Bureau of Public Roads (BPR) functions are developed to model the travel times of links with and without physical separation between vehicle lanes and bicycle lanes. This study also analyzes the conditions for uniqueness of the equilibrium solution. A Newton method is developed to solve the proposed model. Numerical examples with different scales are used to validate the proposed model. The results show that electric bicycles are more favored by travelers during times of high network congestion. In addition, total system travel time can be reduced significantly through physical separation of vehicle lanes from electric bicycle lanes to minimize their mutual interference.

Comprehensive safety assessment in mixed fleets with connected and automated vehicles: A crash severity and rate evaluation of conventional vehicles

Connected and Automated Vehicle (CAV) technology, although in the development stage, is quickly expanding throughout the vehicle market. However, full market penetration will most likely require considerable planning as key stakeholders, manufacturers, consumers and governing agencies work together to determine optimal deployment strategies. Specifically, road safety is a critical challenge to the widespread deployment and adoption of this disruptive technology. During the transition period fleets will be composed of a combination of CAVs and conventional vehicles, and therefore it is imperative to investigate the repercussions of CAVs on traffic safety at different penetration rates. Since crash severity and frequency in conjunction reflect traffic safety, this study attempts to investigate the effect of CAVs on both crash severity and frequency through a microsimulation modelling exercise. VISSM microsimulation platform is used to simulate a case study of the M1 Geelong Ring Road network (Princes Freeway) in Victoria, Australia. Network performance is evaluated using performance metrics (Total System Travel Time, Delay) and kinematic variables (Speed, acceleration, jerk rate). Surrogate safety measures (time to collision, post encroachment time, etc.) are examined to inspect the safety in the network. The results indicate that the introduction of CAVs does not achieve the expected decrease in crash severity and rates involving manual vehicles, despite the improvement in network performance, given the demand and the set of parameters used in our operational CAV algorithm are intact. Additionally, the study identifies that the safety benefits of CAVs are not proportional to CAV penetration, and full-scale benefits of CAVs can only be achieved at 100 % CAV penetration. Further, considering network efficiency as a performance metric and total crash rate involving conventional vehicles as a safety metric, a Pareto frontier is extracted, for varying CAV operational behaviour. The results presented in this study provide insights into the impacts of CAVs on traffic safety valuable for insurance companies and other industry participants, enabling safety-related services and more enterprising business models.