Link Travel Time Research Articles

Regrets in Routing Networks

The impact of the recent increase in routing apps usage on road traffic remains uncertain to this day. The article introduces, for the first time, a criterion to evaluate a distance between an observed state of traffic and the user equilibrium of the traffic assignment: the average marginal regret . The average marginal regret provides a quantitative measure of the impact of routing apps on traffic using only link flows, link travel times, and travel demand. In non-atomic routing games (or static traffic assignment models), the average marginal regret is a measure of selfish drivers’ behaviors. Unlike the price of anarchy , the average marginal regret in the routing game can be computed in polynomial time without any knowledge of user equilibria and socially optimal states of traffic. First, this article demonstrates on a small example that the average marginal regret is more appropriate to define proximity between an observed state of traffic and an user equilibrium state of traffic than comparing flows, travel times, or total cost. Then, experiments on two different models of app usage and three networks (including the whole L.A. network with more than 50,000 nodes) demonstrate that the average marginal regret decreases with an increase of app usage. Sensitivity analysis of the equilibrium state with respect to the app usage ratio proves that the average marginal regret monotonically decreases to 0 with an increase of app usage. Finally, using a toy example, the article concludes that app usage could become the new Braess paradox.

Network-Wide Link Travel Time Inference Using Trip-Based Data From Automatic Vehicle Identification Detectors

With the continuous expansion of transportation networks in the urbanization process, monitoring network-wide traffic dynamics with the link resolution has become a significant challenge. Moreover, applying the traditional link-based data into traffic states identification over large-scale networks requires expensive costs. Fortunately, nowadays, we can collect large amounts of trip-based data which makes it possible to tackle this problem. In this paper, we propose a probabilistic framework to infer network-wide link travel time with trip-based data from automatic vehicle identification (AVI) detectors. Different from the link-based data, the AVI data contains multiple attributes, such as trip origin, destination, time stamps, and vehicle license plate. Thus, it is more possible to infer individual traces at a network-wide scale with the AVI detectors. In the proposed framework, we first develop the probabilistic trip travel time allocation (PTTA) model to assign the trip travel time of each vehicle into its traversed links. Especially, the combination impacts of signal control and traffic flow on link travel time are introduced in this model for each allocation. Then, we extract available allocations covered by divided intervals for real-time estimation of network-wide average link travel time. A real-world data set was collected from Xuancheng, Anhui, China. The empirical results show that the performance of PTTA model has a superior level on allocation accuracy over similar methods, and the proposed framework is proved to be reliable at a network-wide scale when the AVI detection sparsity of network is within a certain range.

Spatiotemporal Congestion Recognition Index to Evaluate Performance under Oversaturated Conditions

We developed a congestion index for traffic management that uses link travel time based on global positioning system probe vehicle data. The congestion index can be used to evaluate the performance of an arterial network under oversaturated conditions. It considers congestion intensity and congestion duration, unlike existing congestion indices. After investigating current big traffic data, we selected the average link travel speed that could be collected at all times over a wide range to develop the congestion index. We used extra travel time as a new measure of effectiveness. The congestion criterion for monitoring oversaturated conditions was set as extra travel time with three congestion levels when traffic flow exceeded capacity. We devised the new congestion index by reflecting the three congestion levels to monitor oversaturated conditions macroscopically and to examine congestion characteristics microscopically. A spatiotemporal congestion recognition index (SCRIN) was defined based on congestion intensity and congestion duration. We analyzed SCRIN to monitor the traffic congestion of Seoul city using the data on travel speeds from the seoul transport operation and information service (TOPIS) for 2016.

Method for Reliable Shortest Path Determination in Stochastic Networks using Parametrically Defined Stable Probability Distributions

An increase in the number of vehicles, especially in large cities, and inability of the existing road infrastructure to distribute transport flows, leads to a higher congestion level in transport networks. This problem makes the solution to navigational problems more and more important. Despite the popularity of these tasks, many existing commercial systems find a route in deterministic networks, not taking into account the time-dependent and stochastic properties of traffic flows, i.e. travel time of road links is considered as constant. This paper addresses the reliable routing problem in stochastic networks using actual information of the traffic flow parameters. We consider the following optimality criterion: maximization of the probability of arriving on time at a destination given a departure time and a time budget. The reliable shortest path takes into account the variance of the travel time of the road network segments, which makes it more applicable for solving routing problems in transport networks compared to standard shortest path search algorithms that take into account only the average travel time of network segments. To describe the travel time of the road network segments, it is proposed to use parametrically defined stable Levy probability distributions. The use of stable distributions allows replacing the operation of calculating convolution to determine the reliability of the path to recalculating the parameters of the distributions density, which significantly reduces the computational time of the algorithm. The proposed method gives a solution in the form of a decision, i.e. the route proposed in the solution is not fixed in advance, but adaptively changes depending on changes in the real state of the network. An experimental analysis of the algorithm carried out on a large-scale transport network of Samara, Russia, showed that the presented algorithm can significantly reduce the computational time of the reliable shortest path algorithm with a slight increase in travel time.

Evaluation of Transportation System Resilience in the Presence of Connected and Automated Vehicles

Large-scale natural disasters challenge the resilience of the surface transportation system. The objective of this research was to develop a resilience model of the surface transportation system with a mixed-traffic environment and considering varying Connected and Automated Vehicle (CAV) penetration scenarios. As deployment of CAVs is expected to improve traffic operations, a resilience model was developed in this research to evaluate the resilience performance of a transportation system with several CAV penetration levels (0%, 25%, 50%, 75%, and 100%) for a given budget and recovery time. The proposed resilience quantification model was applied on a roadway network considering several disaster scenarios. The network capacity in relation to trips at any phase of disaster was compared with the pre-disaster trips to determine the system resilience. The capacity variation and the travel time variation were also estimated. The analysis showed that the resilience of the transportation system improved with CAVs in relation to travel time and capacity improvement. Link travel times were significantly improved by higher CAV penetration rate. The findings also suggested that higher penetration of CAVs (i.e., 50% or more) increased the recovery costs. For example, the recovery costs needed for medium and large-scale disasters were 50% and 90% higher, respectively, compared with the recovery costs for a small-scale disaster. These higher costs were primarily for the repair and replacement of intelligent infrastructure required to support the operation of CAVs.

Comparative evaluation of measures for urban highway network resilience due to traffic incidents

Abstract The resilience of an urban highway network may be described as the ability for a highway network’s operation to adapt and rapidly recover from a disruptive event. Although the concept of resilience has been studied in the past decades, researchers have yet to agree on common measures of quantifying urban highway network resilience. This paper proposed five candidate measures of urban highway network resilience that are consistent with the concept of resilience triangle. They are derived from queue length, link speed, link travel time, frontage road delay and detour route delay, respectively. These measures were calculated from outputs of a mesoscopic traffic simulation model that mimicked the highway network in the El Paso, Texas region. Thirty scenarios were simulated, each with a complete link closure at a selected major highway location caused by a traffic incident. The results have shown that the five measures are not statistically correlated with each other, and the different measures produced different ranked lists in decreasing order of the impacts of the links closure locations. This means that using different resilience measures will lead to different conclusions in the order of disruption caused by the link closures. The outcomes supports the notion that a common measure of transportation highway resilience may not be necessary, and researchers may define their own resilience measures to meet individual project’s need.

Freeway travel time estimation based on the general motors model: a genetic algorithm calibration framework

Travel time estimation plays an important role in freeway performance assessment and reliability management. Conventional estimation methods based on speed information do not consider the level of congestion during modelling, leading to unreliable estimations under congested conditions. This study presents a freeway travel time estimation method based on the general motors (GM) model, where the concept ‘virtual vehicle’ was proposed to apply macroscopic traffic flow data to the microscopic GM model. First, a GM-based travel time estimation (GMTTE) model was developed to estimate link travel time. Three sets of parameters l , m and α were separately used under free-flowing, congestion and transition conditions. Next, two corridor estimation models, namely GMTTE time-slice-based (GMTTE-TSB) model and GMTTE continuous-speed (GMTTE-CS) model, were proposed. To calibrate the three parameters, a genetic algorithm framework was developed. The proposed method was calibrated and evaluated using traffic data collected from seven freeway segments in the greater St. Louis, Missouri region. Results show that (i) the GMTTE-CS model outperformed the GMTTE-TSB model; (ii) the optimal parameters of the GMTTE-CS model under free-flowing and congested conditions are l = 1.1 , m = 2.0 , α = 8.0 and l = 1 , m = 0.1 , α = 8.0 , respectively; (iii) the GMTTE-CS model outperformed the instantaneous and time-slice models under congested conditions, while showing similar accuracies with the two models under free-flowing and status-transition conditions; and (iv) the optimal time interval was found to be 9–10 min for free-flowing conditions and 6–7 min for congested conditions.

An energy-efficient reliable path finding algorithm for stochastic road networks with electric vehicles

In this paper, we develop a novel reliable path finding algorithm for a stochastic road network with uncertainty in travel times while both electric vehicle energy and efficiency are simultaneously taken into account. We first propose a bi-objective optimization model to maximize (1) the on-time arrival reliability and (2) energy-efficiency for battery electric vehicles (BEVs) in a path finding problem. The former objective requires finding the reliable shortest path (RSP), which is the path with the minimal effective travel time measured by the sum of the mean travel time and a travel time safety margin for any given origin-destination (OD) pair. Then, we refer to energy-efficiency as the minimum of the electric energy consumption. We discuss the non-additive property of the RSP problem since we also consider the link travel time correlations, whereas the latter objective satisfies the additive criterion. To this end, we illustrate the existence of non-dominated solutions that satisfy both of the two objectives. Furthermore, it is shown that the intersection of two candidate sets – one for the RSPs and the other for paths with minimal energy-consumption - actually contains the optimal solution for the bi-objective optimization problem. The upper and lower bounds of the effective travel time are mathematically deduced and can be used to generate the candidate path set of this bi-objective problem via the K-shortest algorithm. Our proposed algorithm overcomes the infeasibility of traditional path finding algorithms (e.g., the Dijkstra algorithm) for RSPs. Moreover, using two numerical examples, we verify the effectiveness and efficiency of the proposed algorithm. We numerically demonstrate promising potential applications of the proposed algorithm in real-life road traffic networks.

Minimum Sampling Size of Floating Cars for Urban Link Travel Time Distribution Estimation

Despite the wide application of floating car data (FCD) in urban link travel time estimation, limited efforts have been made to determine the minimum sample size of floating cars appropriate to the requirements for travel time distribution (TTD) estimation. This study develops a framework for seeking the required minimum number of travel time observations generated from FCD for urban link TTD estimation. The basic idea is to test how, with a decreasing the number of observations, the similarities between the distribution of estimated travel time from observations and those from the ground-truth vary. These are measured by employing the Hellinger Distance (HD) and Kolmogorov-Smirnov (KS) tests. Finally, the minimum sample size is determined by the HD value, ensuring that corresponding distribution passes the KS test. The proposed method is validated with the sources of FCD and Radio Frequency Identification Data (RFID) collected from an urban arterial in Nanjing, China. The results indicate that: (1) the average travel times derived from FCD give good estimation accuracy for real-time application; (2) the minimum required sample size range changes with the extent of time-varying fluctuations in traffic flows; (3) the minimum sample size determination is sensitive to whether observations are aggregated near each peak in the multistate distribution; (4) sparse and incomplete observations from FCD in most time periods cannot be used to achieve the minimum sample size. Moreover, this would produce a significant deviation from the ground-truth distributions. Finally, FCD is strongly recommended for better TTD estimation incorporating both historical trends and real-time observations.

Steady-state link travel time methods: Formulation, derivation, classification, and unification

Travel times are one of the most important outputs of transport planning models, especially in a strategic context. It is therefore paramount that the methods that underpin the construction of travel times are well understood. A plethora of methods exists to extract and/or construct travel times given some underlying network loading procedure, also known as the traffic flow propagation model. However, the relation between these different travel time methods and the consistency between such methods has received relatively little attention in the literature. This might in part be due to the many different traffic flow propagation models in existence, ranging from vehicle based (microscopic), to flow based (macroscopic), and models that explicitly account for the time varying nature of traffic flows (dynamic) to models that do not (static). In this work, we limit ourselves to flow based, i.e. macroscopic, traffic flow models. Within this modelling paradigm we consider dynamic, semi-dynamic, and static traffic flow propagation formulations used to construct link travel times. The semi-dynamic and static approaches are considered as more aggregate versions of the dynamic formulation. Within this context we formulate a unified (link) travel time formulation that is consistent across these three modelling paradigms under the assumption of steady-state flow conditions. The dynamic link travel time formulation is based on a recent state-of-the-art continuous time macroscopic dynamic network loading model. In the dynamic model we assume steady-state conditions to remain consistent with steady-state semi-dynamic and static approaches. This allows us to derive semi-dynamic link travel time formulations from our dynamic model, while the static formulation is derived from its semi-dynamic counterpart. Throughout this work we explore link travel times from three different perspectives; an experienced perspective, which actively tracks the tail of a physical queue, and two functional perspectives, both of which do not require explicit queue tracking. Based on the existing literature and proposed formulations, a classification framework is proposed allowing one to compare existing (and future) methods in the literature in an objective fashion. We provide a number of explicit derivations of existing model formulations that can be considered special cases of our unified approach. A numerical example across the different perspectives is included and a significant number of representative existing methods in the literature has been classified based on our proposed framework for the reader's convenience.

A Boundedly Rational Day-to-Day Rerouting Model with Dynamic Indifference Band and Net Marginal Cost Preference

This paper suggests factoring both dynamic indifference band (DIB) and marginal decision rule into boundedly rational day-to-day traffic dynamics modeling. To this purpose, a relative indifference band based DIB is employed to capture the variation of indifference band against dynamical network traffic conditions, and the net marginal cost preference (NetMCP) is newly introduced and quantified to replace the routine marginal cost preference as an amended behavior criterion for rerouting decision. To concentrate on the essence, DIB and NetMCP are mathematically formulated into the traffic evolution equation of the classical proportional-switch adjustment process with a new revision protocol, leading to the present dynamically bounded rational swapping process with NetMCP (DBRSP-NetMCP), of which the stationary state is defined and proved. It is also proved that DBRSP-NetMCP is a bounded rational behavior swapping process; and given separable link travel times, DBRSP-NetMCP is globally stable. It is numerically demonstrated that i) an object-oriented modelling approach may cause obvious deviation in rerouting behavior from the behavior-oriented one, and thus is inadvisable to model the day-to-day traffic dynamics; and ii) both DIB and NetMCP show striking effect on traffic evolution, and NetMCP helps to shorten the evolution path towards the stationary states, which may contribute to better the performance of a swapping algorithm to solve the deterministic traffic equilibrium.

Dynamic identification of urban traffic congestion warning communities in heterogeneous networks

Network-wide traffic control strategies (e.g. perimeter control and route guidance) in urban networks have recently been mainly studied to relieve or postpone congestion based on the theory of macroscopic fundamental diagram (MFD). Nevertheless, these studies are mostly applied to the statically partitioned networks or the dynamic networks that fail to fully consider traffic state prediction, conflicting with strongly spatiotemporal variability of traffic congestion or objective of active traffic management (ATM). This paper proposes a methodology to dynamically identify critical congestion warning areas from heterogeneous urban road networks, which aids to design efficient perimeter control approaches. In the methodology, a dynamic directed weighted network is built on the base of the link connectivity and the real-time traffic loads, and a link travel time prediction method based on Kalman filter is developed to calibrate directed weight values and undirected input values for links. With the undirected link input information, a dynamic congestion warning community detection method which consists of three consecutive steps is developed. Firstly, it could capture emergence of new congestion areas based on the definition of congestion seed intersection. Secondly, expansion and regression of each congested area could be achieved with the objectives of spatial compactness and traffic condition homogeneity. Thirdly, a two-level merging algorithm of adjacent different congestion areas is designed utilizing modularity model in community detection of complex networks. The proposed methodology is validated using ground truth data from downtown network of Jinan City in China. The results show that the proposed algorithms can efficiently track congestion evolutionary processes and effectively detect congestion warning areas from the test real network.

Research on Evaluation and Prediction Method of Link Travel Time Based on Floating Car Data by Simulation

Using the floating car data obtained by simulation to carry out theoretical research on link travel time evaluation and prediction, it has the advantage of conveniently setting different situations and providing reference for practical applications. In this paper, we have acquired the floating car data based on microscopic simulation, and then the complex trapezoidal quadrature formula is used to obtain link travel time of a single vehicle. When setting different ratios of floating cars, the average link travel time is obtained by the mean-median method. The results show that the effect is better than the arithmetic average method, and the higher the ratio of floating cars, the higher the accuracy of evaluation. The markov model has been used to correct the gray prediction model, with that we establish travel time prediction model based on the grey markov chain. The experimental results show that the prediction accuracy is high.

Improve traffic efficiency with advanced travel time feedback in urban networks

With the development of modern intelligent traffic system technology, the travel time information can be collected and processed to provide route-choice suggestions to travellers. However, due to the complex nature of a traffic system, the feedback of traffic information might lead to undesired congestion in some concerned areas (such as the central business district). In this paper, an improved time shortest path strategy (ITSP) based on advanced travel time information feedback is proposed and applied in a Manhattan-like urban traffic system. With the strategy, the link travel time in the concerned area is adjusted by a travel-cost-related coefficient before being sent to final users. We study the effects of ITSP on traffic performance based on cellular automaton model of urban traffic. What we found most interesting is that when providing the traffic time information with an slightly larger than 1.0 (typically ), the performance of the system will be enhanced as compared to the situations of no information feedback or providing the real travel time information. Simulation results show that, ITSP can increase the average arrival rate and the traffic flow in scenario of fixed total number of vehicles. Vehicle density decreases with ITSP strategy under scenario with varying total number of vehicles, which helps to avoid traffic gridlock and improve the system reliability. Furthermore, the effects of different size of core areas and different origin-destination patterns are also explored. All the results show that ITSP can improve the traffic performance of network systems.

Lane-based signal optimization with left turn prohibition in urban road networks

Left turns may generate efficiency problems, which can be solved by appropriately prohibiting left turns. The goal of this paper is to propose a method for purpose of minimizing total travel times in urban road networks by prohibiting left turns. With left turn prohibition, the signal timing plan is optimized with the lane-based method because the method can adequately handle both signal timing optimization and lane assignment. The total travel time is calculated with link flows and link travel time being estimated with signal settings. As illustrated by numerical examples, prohibiting left turns reduces the total travel time of car traffic in road networks. As the left turn prohibition results can handle the randomness in the network, these results provide potential implications for congestion management.

Determining time-dependent minimum cost paths under several objectives

As the largest contributor to greenhouse gas (GHG) emissions in the transportation sector, road freight transportation is the focus of numerous strategies to tackle increased pollution. One way to reduce emissions is to consider congestion and being able to route traffic around it. In this paper we study time-dependent minimum cost paths under several objectives (TDMCP-SO), in which the objective function comprises GHG emissions, driver and congestion costs. Travel costs are impacted by traffic due to changing congestion levels depending on the time of the day, vehicle types and carried load. We also develop time-dependent lower and upper bounds, which are both accurate and fast to compute. Computational experiments are performed on real-life instances that incorporate the variation of traffic throughout the day, by adapting Dijkstra’s label-setting algorithm according to different cost computation methods. We show that explicitly considering first-in, first-out (FIFO) consistency using time-varying speeds allows the efficient computation of tight time-dependent bounds. Our computational results demonstrate that the TDMCP-SO is more difficult to solve to optimality but the proposed algorithm is shown to be robust and efficient in reducing the total cost even for large instances in an environment of varying speeds, outperforming those based on the link travel time model and on the smoothing method according to each optimization objective, flexible departure times, and different load patterns.

An algorithm for reliable shortest path problem with travel time correlations

Reliable shortest path (RSP) problem reflects the variability of travel time and is more realistic than standard shortest path problem which considers only the average travel time. This paper describes an algorithm for solving the mean-standard deviation RSP problem considering link travel time correlations. The proposed algorithm adopts the Lagrangian substitution and covariance matrix decomposition technique to deal with the difficulty resulting from non-linearity and non-additivity of the Mixed Integer Non-Linear Program (MINLP). The problem is decomposed into a standard shortest path problem and a convex optimization problem whose optimal solution is proved and the Lagrangian multipliers ranges are related to the eigenvalues of the covariance matrix to further speed up the algorithm. The complexity of the original problem is notably reduced by the proposed algorithm such that it can be scaled to large networks. In addition to the sub-gradient Lagrangian multiplier updating strategy integrated with projection, a novel one based on the deep-cut ellipsoid method is proposed as well. Numerical experiments on large-scale networks show the efficacy of the algorithm in terms of relative duality gap and computational time. Besides, there is evidence showing that, though having longer computational time, the ellipsoid updating method tends to obtain better solutions compared with the sub-gradient method. The algorithm outperforms the existing one-to-one Lagrangian relaxation-based RSP algorithms and the exact Outer Approximation method in the literature.

Bandwidth optimization for lane-based traffic signal settings with improved stopping criteria in non-linear TRANSYT delay

While assigning given OD demand flows onto network links, travel times along different routes could be varied depending on respective traffic volumes. To achieve equilibrium, all used routes should have the minimum and identical travel times. Such route travel times are composed by link travel times and end of link delay. Upstream and downstream traffic signals are coordinated through bandwidth maximization. Path flows and path travel times are modeled to be responsive to traffic signal settings to enable attractive path choices. Approximated linear function will be established to linearize the end of link delay in TRANSYT model. The problem is formulated as a Binary-Mixed-Integer-Linear-Program and could be solved by standard branch-and-bound method.

Short-Term Urban Link Travel Time Prediction Using Dynamic Time Warping With Disaggregate Probe Data

There is increasing demand for short-term urban link travel time prediction to build an advanced intelligent transportation system (ITS). With the development of data collection technology, probe data are receiving more attention but the penetration rate of probe vehicles capable of sending probe data is still limited. Most research pertaining to short-term travel time prediction tends to aggregate probe data to obtain useful samples when the penetration rate is low. However, as a result, the prediction can only provide a general description of the travel time and changes in travel time during a short time interval are neglected. To overcome this limitation, a non-parametric model using disaggregate probe data based on dynamic time warping (DTW) was developed in this study. Data from the crossing direction are introduced to separate the data into different signal phases instead of identifying the exact signal pattern. A classical k-nearest neighbor (KNN) model and a naive model were compared with the proposed model. The models were tested in three scenarios: a computer simulation and two real cases from Nagoya, Japan. The results showed that the proposed model outperforms the other two models under different data penetration rates because it can reflect changes in travel time during a traffic signal cycle. Moreover, the proposed model has wider applicability than the KNN model because it is free from the equal time interval constraint.

Validation of an agent-based travel demand model with floating car data

Abstract This paper compares the results of the agent-based travel demand model MITO (Microscopic Travel Demand Orchestrator) with Floating Car Data. MITO is developed using household travel survey data, and uses the traffic assignment model MATSim. The model estimates the travel demand for an average working day and is applied to the metropolitan area of Munich. In contrast to traditional approaches where travel demand models are validated using the local traffic counts, average travel speed from Floating Car Data (FCD) are used in this study. The main advantage of using FCD is that they cover extremely large parts of the network, whereas the local traffic counts are sparse and limited to a few major streets. The average link travel time and average speed between the model estimation and the FCD were compared with the goal of validating travel time calculations within an agent-based transport model.