Free-flow Travel Time Research Articles

Stochastic queue profile estimation using license plate recognition data

Queue profile has garnered significant attention for its precise description of queue formation and dissipation at signalized intersections. License Plate Recognition (LPR) systems, recording full-sample lane-based departure times of individual vehicles, offer an ideal data source for queue profile estimation. Thus, this paper presents a stochastic queue profile estimation method utilizing LPR data. It adopts the classic Input-Output model as a baseline to reconstruct arrival and departure curves using departure information from LPR detectors at successive intersections. A pseudo departure curve is then derived from the distribution of free-flow travel time. The stochastic queue profile is further reconstructed by analyzing the relationship among these curves, accounting for potential lane changes and overtaking behavior. The proposed method is validated through empirical and simulation cases. The empirical case achieves accurate queue length estimation with a Mean Absolute Error (MAE) of 5.38 m, while the simulation case yields precise stochastic queue profiles with an MAE of 5.95 m and an average deviation of 2.40 m for queue length. Sensitivity analysis demonstrates the method's robustness across parameters like demand-to-capacity ratio, miss detection ratio, and overtaking ratio. This work holds promise for real-time signal control optimization and vehicular trajectory reconstruction.

A hazard-based model to derive travel time under congested conditions

Increasing congestion levels and their elusive impact warrants the development of congestion mitigation strategies. Quantifying congestion and analyzing the spatiotemporal variations is imperative to achieve this target. Travel time is being explored as a measure of congestion with the advent of Intelligent Transportation Systems (ITS) and the deployment of related technologies. Researchers identified congested conditions when the average travel time exceeds 1.33 or 1.66 times free-flow travel time (FFTT). It is well known that travel time under congested conditions (Tc) is more sensitive to land use, road geometry, and traffic control characteristics than the FFTT. Therefore, the extension of FFTT to derive Tc may not be appropriate. This study focuses on developing a hazard-based model to derive Tc. Travel time is modeled using a parametric accelerated failure time (AFT) model. The applicability of the proposed methodology is justified using empirical and simulated datasets. The Tc derived from the AFT model is close to the travel time for the level of service (LOS F). Based on the Tc, a new measure of congestion, termed congestion index (CI), is proposed. The proposed index can quantify the frequency and intensity of congestion on a link or network. The traffic states identified based on CI were mapped on the fundamental diagram (FD) and the macroscopic fundamental diagram (MFD). It was concluded that if travel times are uncertain and unstable under low-density conditions, then the ascending leg of the FD or MFD can be marked congested. Uncertain and unstable travel times indicate that traffic flow is unstable, and therefore, it can be concluded that traffic instabilities significantly affect congestion.

Travel Time Reliability Analysis in Three Different Routes in Baghdad City

Travel time reliability is defined as the degree of certainty and predictability of travel time on a transportation system. Reliable transportation systems provide some guarantees to reach a particular destination within a reasonable time in harmony with the expected time. In this study data collection process on the specified paths was carried out through the test vehicle equipped with GPS (moving vehicle technology) device. The Statistical Package program(SPSS) was used to analyse the total travel time of each link on the three selected routes and the estimated speeds based on GPS field data collected during 50 running trials in the south and north directions during morning and evening hours. The results of buffer time indexes for three routes are presented for each route from Bayaa to Bab Almuadam to illustrate changing structure of the reliability during the peak period. Extra delays with regard to free flow travel time are observed on route 1, 2, and 3 in terms of 95% percentile travel time about 52, 38, and 41%, respectively.

A Real-Time Prediction Model for Individual Vehicle Travel Time on an Undersaturated Signalized Arterial Roadway

Predicting the segment travel time of a vehicle, which includes its travel time on a road segment and the delay for passing the downstream intersection, can greatly help the drivers to better control a vehicle’s speed to reduce signal delay and gasoline consumption. However, so far, few effective methods have been developed that can accurately characterize an individual vehicle’s segment travel time. To address this problem, this article seeks to develop an analytical model to predict each vehicle’s travel time on a signalized arterial roadway under nonsaturated traffic conditions. The proposed model is developed based on the following three situations a vehicle can experience when passing through a signalized intersection: 1) passing directly in the green time phase, 2) decelerating until coming to a complete stop in the red intervals, and 3) decelerating initially, but passing through without a stop. The situation that a vehicle will encounter is predicted by analyzing the correlation between a vehicle’s entry time to the segment and the signal-phase timing using the kinematic wave theory. Then, the intersection delay of each vehicle is predicted based on the queue shockwave speed and the discharge shockwave speed. A vehicle’s travel time on a road segment is predicted by summing the intersection delay and the segment free-flow travel time. The numerical application shows that the proposed model can accurately characterize a vehicle’s travel time. Thereby, it can be applied in a connected environment to predict the segment travel time to improve the mobility of the traffic.

Compatibility between delay functions and highway capacity manual on Iraqi highways

Abstract Volume delay functions (VDFs) are mathematical relationships used by the traffic allocation step of demand forecasting models to take into account the effect of increased traffic flow on the time spent to travel each possible route between different travel sources and destinations. The VDF is usually applied in static traffic assignment to describe the resultant link travel times, as a function of flow and capacity and free-flow travel time. This study aims to investigate the interface between the delay functions used by demand forecasting models and the highway capacity manual (HCM) model flow-speed relationships. The most commonly used VDFs in transport demand modeling packages in the literature were identified. The Bureau of Public Roads (BPR), conical functions (CF), Akçik and Troutbeck function (ATF), and delay logistics function (LF) were described. The four VDFs and the current HCM models were calibrated for the Iraqi road environment, and their compatibilities were examined. Results show that the best adjustments were obtained using the BPR function (quadratic error 0–0.012) and LF (quadratic error 0–0.002). The roles of CF and ATF were used with care, as both appear to neglect the delay in the condition of small to medium traffic patterns typical to country roads. Particularly, in response to single-lane roads, the LF has proven to be useful due to its potential to represent significant delays for low traffic flows and simultaneously produce more delays in congestion conditions; furthermore, the effect of flow as well as intersection spacing is obviously nonlinear. As flow reaches 600 pcu/h/lane, running time increases quickly. With more intersections per kilometer, the impact is obviously greater.

Choice set robustness and internal consistency in correlation-based logit stochastic user equilibrium models

The Stochastic User Equilibrium (SUE) traffic assignment model is a well-known approach for investigating the behaviours of travellers on congested road networks. SUE compensates for driver/modelling uncertainty of the route travel costs by supposing the costs include stochastic terms. Two key challenges for SUE modelling, however, are capturing route correlations and dealing with unrealistic routes. Numerous correlation-based SUE models have been proposed, but issues remain over both internal consistency and choice set robustness. This paper formulates (and proves solution existence for) new internally consistent SUE formulations of GEV structure and correction term logit route choice models, where the functional forms in the correlation components are based upon generalised, flow-dependent congested costs, rather than e.g. length / free-flow travel time as done typically. Numerical experiments are then conducted on the Sioux Falls and Winnipeg networks, where computational feasibility for obtaining internally consistent solutions, choice set robustness, and internal consistency are assessed/compared.

Empirical Analysis for Measuring Travel Time Reliability on Road Network

This research analyses the characteristics of travel time reliability for the road network in Kota Bandar Lampung. Therefore, travel time consists of access, wait and interchange time, while its reliability deals with variations of in-passenger/private cars time. Survey of travel time on each road was carried out for 12 hours (from 06.00 to 18.00) for five working days. Furthermore, the buffer time method was used to measure the characteristics of time travel reliability consisting of five measuring tools, namely planning time, planning time index, buffer time, buffer time index and travel time index. This research found that the temporal effects are the main factor that tends to affect travel time, whereas network effects are the second factor that tends to affect travel time. Furthermore, the regression equation was developed to express the effect of planning time (TPlan) and free-flow travel time on average travel time .

The value of pre-trip information on departure time and route choice in the morning commute under stochastic traffic conditions

Uncertainty in transportation systems can incur additional travel costs, but this adverse effect can be mitigated by providing travel information (Lindsey et al., 2014). It is not clear, however, if providing information always reduces system travel costs, particularly when the information provided is not one hundred percent accurate. This paper studies the welfare effects resulting from providing pre-trip information to morning commuters in a two-route network with bottlenecks, where the bottleneck capacity and free-flow travel time on each route are stochastic. We derive the expected travel costs at user equilibrium (UE) under stochastic conditions without and with pre-trip information first on one route, and later on two routes. Under this model, we examine how accurate and inaccurate information affect commuting costs. We find that full and accurate information is welfare-improving under stochastic bottleneck capacity and deterministic free-flow travel time. However, when bottleneck capacity and free-flow travel time are both stochastic, full and accurate information can be welfare-reducing for the two-route scenario. We show that the degree of correlation between routes in traffic conditions, the frequency and severity of bottleneck capacity drops, and the relationship between free-flow travel time and bottleneck capacity significantly affect the welfare effects of pre-trip information. Furthermore, when bottleneck capacity experiences severe drops, full information is more likely to be welfare-improving, even if the provided information is not completely accurate. These theoretical results are supplemented by numerical examples that show the welfare effects of providing pre-trip information to morning commuters.

Travel Time Prediction Models for Major Arterial Road in Baghdad City using Manufactured GPS device

Travel time on arterials can be rotten into two components, free flow travel time and delay. That’s where the difference between the observed travel time and travel time of free flow, the delay can be calculated. Which is one of the most important roadways Metrics for Traffic. Estimation of travel time and measurement of reliability are critical issues for improving the efficiency of traffic network operations and safety. The purpose of this review is to estimate overall travel time and to analyse the collected data and also to estimate the travel time through predicting model for total delay of signalized intersections. In addition, utilizing a more convenient method and tools to reflect and to achieve a more practical model of delay time for the selected urban arterial in Baghdad city. Global Positioning System (GPS) receivers have an ability to reliably measure intersection delay in terms of (deceleration delay, stopped delay, and acceleration delay). In addition to measuring the travel time for each link along the streets specified for this study (Al-Karada Kharidge street). 50 test runs were collected from GPS-equipped instrumented vehicle at off peak and peak hours of the day for each direction for selected street in this study. So, the required data collection period for two months was from December 2019 to February 2020 were identified to study the complete travel time variations and travel delay times for each link and each intersection along the identified arterial street. The field data were collected for the selected of Al-Karada Kharidge street the Baghdad University intersection to Mohamed al-Qasim intersection.

Reliability Assessment of Road Network Operation

In the frame of the project «Criteria of traffic control quality and effectiveness in Russian Federation» the Transportation Laboratory of ISTU was invoved into elaboration of network performance appraisal. Two alternative congestion measures were considered. The ratio of peak period to free flow travel times was proposed as a test criterion of urban highway network reliability.

The General System Model of Vehicle TDM Effect on Urban Road Traffic

Rapid growing congestion in urban areas lead to wide adoption of travel demand management (TDM) policies. However, the effect evaluation of TDMs encounters great difficulty under existing traffic simulation models which in common require a complex road link system to be built and accurate attributes to be assigned to each link. To avoid the fallible and difficult establishing and calibrating process of existing methodologies, a novel way to analyze TDM is proposed in this paper. With travel demand being described as free-flow travel time of a trip and several macroscopic concepts defined, a theoretical methodology is proposed to reveal the interactions between supply and demand. Then the simulation of TDM is transformed into a classic state space analysis problem. Based on this methodology, the general system model of TDM effect on road traffic operation is set up. The proposed model is tested with three TDMs, including driving restriction, staggered shifts and change of trip length distribution. The results prove that the proposed model can provide fast analysis for TDM policies and has a broad application prospect in macro traffic simulation.

Urban traffic assignment based on signalized intersection delays

Existing methods to determine travel time on the road network links are based on the estimation of link saturation. Different formulas are used during the estimation but the ratio of traffic volume to link capacity is always the base for calculations. The main problem of such an approach is an insufficient justification of the lane capacity value which defines link capacity. It causes doubts about the background of the approach application regarding urban traffic flows. At the same time, authoritative traffic management guidelines declare that when there are signal-controlled intersections on the link then the link capacity is restricted by the intersection with minimal capacity. Thus, the alternative to the known traffic assignment approach based on link capacity can be created and then used to make the assignment on the base of delays at intersections. Evaluation of such an alternative is the actual task as the new approach to traffic assignment opens the scope for improving the accuracy of modelling the traffic flows in cities. Goal. The new approach to traffic assignment on the base of intersection delays needs to be tested for the usefulness for transport modelling practice. Methodology. The alternative approaches to traffic assignment were experimentally compared based on the parameters of the transport model, which provide the compliance of the calculated average vehicles’ speed with the factual one. Results. The new approach to traffic assignment based on intersection delays allows a more accurate calculation of traffic volumes. Originality. The new approach to traffic assignment based on intersection delays allows avoiding the need to set restrictions on road network links’ capacity. Practical value. Received results clarify the way to set the free-flow travel time of network objects in VISUM and allow increasing accuracy of urban traffic flows modelling

Departure Time and Route Choices With Accurate Information Under Binary Stochastic Bottleneck Capacity in the Morning Commute

Uncertainty, a critical factor of causing congestion and extra travel costs in the commute, can be mitigated by providing information. This paper studies the welfare effects of accurate pre-trip information on departure time and route choices in the morning commute under binary stochastic bottleneck capacity. We consider a classical two-route network. Each route has a single bottleneck where congestion occurs during the rush hours. The two routes’ bottleneck capacities vary from day-to-day due to events such as bad weather, accidents, and temporary road closures. We derive all equilibrium solutions in consideration of the differences between routes in free-flow travel time, the shadow value of travel time, the severity of bottleneck capacity reductions, and the degree of correlation between two routes in travel conditions. Furthermore, we investigate the benefit changes from zero-information to full-information and prove that accurate pre-trip information about the bottleneck conditions is strictly welfare-improving. Finally, these theoretical results are supplemented by case studies that show examples of benefit gains from pre-trip information.

Departure and travel time model for the temporal distribution of morning rush-hour traffic congestion

The chronic traffic congestion undermines the level of satisfaction within a society. This study proposes a departure time model for estimating the temporal distribution of morning rush-hour traffic congestion over urban road networks. The departure time model is developed based on the point queue model that is used for estimating travel time. First, we prove the effectiveness of the travel time model (i.e. point queue), showing that it gives the same travel time estimation as the kinematic wave model does for a road with successive bottlenecks. Then, a variant of the bottleneck model is developed accordingly, aiming to capture travelers’ departure time choice for commute trips. The proposed departure time model relaxes a traditional assumption that the last commuter experiences the free flow travel time and considers travelers’ unwillingness of late arrivals for work. Numerical experiments show that the morning rush-hour generally starts at 7:29 am and ends at 8:46 am with a traffic congestion delay index (TCDI) of 2.164 for Beijing, China. Furthermore, the estimation of rush-hour start and end time is insensitive to most model parameters including the proportion of travelers who tend to arrive at work earlier than their schedules.

System Optimum Fuzzy Traffic Assignment Problem

This paper focuses on converting the system optimum traffic assignment problem (SO-TAP) to system optimum fuzzy traffic assignment problem (SO-FTAP). The SO-TAP aims to minimize the total system travel time on road network between the specified origin and destination points. Link travel time is taken as a linear function of fuzzy link flow; thus each link travel time is constructed as a triangular fuzzy number. The objective function is expressed in terms of link flows and link travel times in a non-linear form while satisfying the flow conservation constraints. The parameters of the problem are path lengths, number of lanes, average speed of a vehicle, vehicle length, clearance, spacing, link capacity and free flow travel time. Considering a road network, the path lengths and number of lanes are taken as crisp numbers. The average speed of a vehicle and vehicle length are imprecise in nature, so these are taken as triangular fuzzy numbers. Since the remaining parameters, that are clearance, spacing, link capacity and free flow travel time are determined by the average speed of a vehicle and vehicle length, they will be triangular fuzzy numbers. Finally, the original SO-TAP is converted to a fuzzy quadratic programming (FQP) problem, and it is solved using an existing approach from literature. A numerical experiment is illustrated.

Establishing an Evacuation Network: A Path Ranking Approach

The evacuation of population exposed to flood hazard requires the establishment of a specific transportation network. This is to compute escape routes to be taken by affected population. At evacuation time, inhabitants of affected area must be evacuated through transportation network to safety area. The main objective of this work is to provide assistance to rescue forces in terms of accessibility by providing itinieraries between buildings at risk of flooding and safety points equipped for this purpose. Technically, the problem of k-shortest paths between two nodes in network has been extensively studied in the literature offering several efficient algorithms for different applications. Those algorithms so-called ranking methods aim to compute the first shortest path then the second one and so on. It’s well known that the computation in ranking methods is based only on one criterion which is generally distance or time. Often a single criterion is not sufficient in some real-world problems (road network, internet, etc.,) where one or several supplementary criteria (such as cost, capacity, security, etc.,) must also be taken into consideration. This multi-criteria optimization so-called labeling method aims to compute a Pareto front that represents a set of non-dominated paths. However it is very difficult to take a decision on which k-paths to select among this set and especially when selected paths are served as a data input for a sensitive problem such as the evacuation. We contribute in this paper to establish an evacuation network dedicated to the evacuation of population exposed to natural hazards and more particularly to flood hazard. Two ranking methods to compute paths between each origin (building) - destination (shelter) pair are presented. The criteria free-flow travel time, capacity and number of lanes of road are considered in computing paths. Thus we aim in the proposed ranking approach to simulate a multi-criteria aspect by combining travel time and number of lanes as weight function. The establishment of network (determination of k-paths between each building and shelter associated) is then performed according to several measures we introduce in this paper.

A generalized Bayesian traffic model

The rapid growth of transportation data offers new opportunities to analyze the interaction between travel behavior and transportation system performance, and particularly when issues such as uncertainty and reliability are considered. Many previous studies in this area described the stationary behavior of stochastic transportation systems using user equilibrium (UE) conditions. In contrast, this paper develops a generalized Bayesian model to analyze the dynamic behavior of stochastic transportation systems. In the proposed model, the variability of link volume and travel time stems from the stochasticity in travel demand, transportation supply (e.g. link capacity, free flow travel time, etc.) and route choice. To the best of our knowledge, this is among the first work that considers the three sources of stochasticity simultaneously. In addition, we propose a Bayesian updating approach based on the Dirichlet model to describe the route choice behavior. This approach allows researchers to consider a wide range of route choice behavior of bounded rationality in day-to-day traffic dynamics, including a knowledge updating mechanism based on different memory lengths and weighting factors. This framework is particularly suitable for data-driven studies supported by emerging transportation data due to the computing efficiency of the Dirichlet-based Bayesian updating mechanism and the sound behavioral foundation. This paper shows that the proposed Bayesian model with infinite memory leads to UE conditions under stochastic demand and supply. Subsequently, a numerical case study is conducted to illustrate different day-to-day route choice dynamics with different memory lengths of system performance. This paper also discusses the influence of the three sources of stochasticity towards the aggregated variance of link volumes and travel time. With enough longitudinal travel choice and transportation system performance data, the proposed Bayesian framework could be empirically calibrated and tested, which offers an attractive descriptive alternative to the conventional UE-based transportation system models.

Exploring alternative spatial weights to detect crash hotspots

Identifying hotspots or crash clusters is an important problem for detecting high-risk locations at which vehicle accidents frequently occur. Several hotspot identification methods have been developed in the literature; however, there are often large differences between the spatial distributions of hotspots obtained by these methods, and spatial weights such as free flow travel times and congested flow travel times have not been fully examined as alternatives to the weight of distance. This paper compares the following commonly implemented network-based hotspot detection methods: Getis-Ord Gi*, Local Moran's I, KLINCS (K-function local indicators of network-constrained clusters), and KLINCS-IC (Inverse Cost) in order to provide insight into understanding the similarities and differences between selected hotspot detection methods when used with different spatial weights. This assessment is performed through using different spatial weights as part of the statistical analysis and comparing them to a prediction accuracy index. Moreover, a sensitivity analysis was conducted based on alternative spatial weights and different parameters to test the effect of bandwidth on the identified hotspots. The findings on the success of alternative spatial weights has a potential to improve the accuracy of the hotspot detection. Results indicate distinct differences in the spatial distributions of hotspots obtained through the considered methods that are based on alternative spatial weights. An interesting finding is that all alternative approaches (spatial weights and bandwidths) cluster when certain bandwidth values are exceeded. From a practical perspective, the CPAI results show that using network-constrained Local Moran's I statistics with a distance-based spatial weights may provide the most feasible approach while implementing safety-focused efforts.

Estimating the social cost of congestion using the bottleneck model

This paper uses the bottleneck model of Vickrey (1969) to empirically measure the social cost of traffic congestion in the US. Using a detailed trip-level data, we estimate extra travel time over and above hypothetical free-flow travel time, which we call “queuing time”, for each average commute trip. The estimated individual queuing time implies that the annual cost of congestion borne by all US commuters is about 29 billion dollars. We find that a higher level of congestion in a city may be attributed to a smaller per capita road stock in the city. This paper also empirically quantifies a toll that depends both on the commuter's arrival time and trip distance.

A trade-off between average and maximum arc congestion minimization in traffic assignment with user constraints

In system optimal traffic assignment of traffic flows with user constraints the total travel time is minimized on a set of paths with bounded length ensuring a certain level of fairness for users. Minimizing the total travel time may lead to experience large travel times on some arcs. On the other hand, when minimizing the maximum arc travel time, the total travel time cannot be controlled. The increase of arc travel time with respect to arc free-flow travel time is related to the arc congestion that is one of the main issues in road networks. In this paper we propose a new model that is a compromise between minimizing the average arc congestion and the worst arc congestion on the road network. The model is a linear program that minimizes the average arc congestion over a given percentage of the most congested arcs. A heuristic algorithm aimed at reducing the number of paths that are considered for the model is also proposed. A computational study is performed that shows the flexibility of the model and the quality of the heuristic.