Travel Time Reliability Measures Research Articles

Exploring the Traffic Congestion and Improving Travel Time Reliability Measures in Heterogeneous Traffic Environments: A Focus on Developing Countries

Exploring the Traffic Congestion and Improving Travel Time Reliability Measures in Heterogeneous Traffic Environments: A Focus on Developing Countries

Travel Time Reliability Prediction Using Random Forests

The concept of travel time reliability was developed to quantify the variability in travel times. As travel time reliability measures are increasingly used in system planning and performance measurement processes at many transportation agencies, predicting travel time reliability measures has become critical. However, it can be challenging because of the dynamic nature of traffic and the variety of factors contributing to unreliable travel times. This paper developed machine learning models to predict travel time reliability at a planning level. Two random forest algorithms, quantile random forests (QRF) and generalized random forests (GRF), were used to develop prediction models while taking account of a variety of variables from multiple data sources simultaneously. The reliability measures studied are the percentiles of travel times as they are a key component for many commonly used travel time reliability measures. Both QRF and GRF models produced accurate predictions; GRF performed better than QRF at predicting the 50th percentile travel time, and QRF achieved slightly better predictions for the 90th percentile. A case study demonstrated the use of the proposed models for estimating the impact on travel time reliability from an improvement project. The results found both models captured the trend in reliability change, and GRF was preferred over QRF for estimating the level of travel time reliability.

Gini Coefficient: A Measure of Travel Time Reliability?

Gini Coefficient: A Measure of Travel Time Reliability?

Selecting the Representative Travel Time Reliability Measure Based on Metric (Dis)Agreement Patterns

Selecting the Representative Travel Time Reliability Measure Based on Metric (Dis)Agreement Patterns

Impacts of COVID-19 on the Operational Performance of Express Lanes and General-Purpose Lanes.

The COVID-19 pandemic outbreak brought significant changes in the travel behavior and operational characteristics of transportation systems. Express lanes (ELs) are among the transportation facilities that are affected by this pandemic. These facilities are built adjacent to existing general-purpose lanes (GPLs), providing drivers additional lanes that are dynamically priced in response to changing traffic conditions. This research investigated the impacts of COVID-19 on the operational performance of ELs and GPLs based on field data from a 5.5 mi corridor on I-95 in Miami, Florida, U.S. The traffic flow parameters, which include speed, traffic flow, and occupancy, were used to describe the traffic conditions before and during COVID-19 (i.e., March–June 2019 and March–June 2020, respectively). The travel time reliability measures, coefficient of variation of travel time, and planning time index, were used to measure user satisfaction. These metrics were derived from a multivariate Bayesian additive regression model that was developed to calibrate the traffic conditions on the study corridor. Overall, the model results indicated that both ELs and GPLs have less variation in travel time, thus making the travel time more reliable during COVID-19 than before. This may be attributed to the decline in the traffic volume observed during the pandemic. The results further showed that COVID-19 had more impact on the GPLs compared with the ELs. The results from this research could assist transportation agencies in understanding the impacts of the COVID-19 pandemic on ELs and GPLs in relation to traffic operations.

How Random Incidents Affect Travel-Time Distributions

We present a novel analytical model to approximate the travel-time distribution of vehicles traversing a freeway corridor that experiences random quality of service degradations due to non-recurrent incidents. The proposed model derives the generating function of travel times in closed-form using clearance time, incident frequency and severity, and other ordinary traffic characteristics. We validate the model using data from a freeway corridor where weather events and traffic accidents serve as the principal causes of service degradation. The resulting model is equivalent in performance to widely used methodologies while uniquely providing a clear connection on how incidents affect travel time distribution. With this connection, the model readily yields travel time reliability measures for alternative roadway behaviors, providing crucial information for long-term planning.

An integrative approach to neighbourhood sustainability assessments using publicly available traffic data

Travel time data and variability of travel times can be used to quantify the network performance as an important aspect of sustainable development in modern cities. We introduce a data collection and analysis framework to be employed as an integral part of a neighbourhood sustainability assessment (NSA). The proposed framework performs a targeted selection and evaluation of indicators based on publicly available traffic data, specifically addressing short-term observations of travel times and traffic counts. Entailing a hybrid and modular approach, we demonstrate the applicability of travel time reliability measures by combining different datasets, various variability estimators, and visualisation methods, to advance the interpretation strength of the assessment. Implemented are regression methods based on cosinor models, which allow us to analyse the rhythmic behaviour of travel time trends. These models can be applied to assess the differential rhythmicity among different factors, like route, weather conditions and type of day (i.e., weekend or workday). Further, the same datasets are used to evaluate two reliability metrics, i.e., a travel time index and a planning time index. We demonstrate the application of the suggested framework on several strategic routes associated with three testbed neighbourhoods in Ljubljana, Slovenia. The results show that average travel times and their variations are affected by route, day of the week, and weather conditions, and correlate with vehicles count data. Analyses of rhythmicity give straightforward insights into travel time trends, appropriate for comparative analysis and thus well-suited for use in NSA and monitoring practices. To the best of our knowledge, this work presents the first study quantifying reliability indicators intended for integration with NSA standards.

Methodology for Predicting MAP-21 Interstate Travel Time Reliability Measure Target in Virginia

This study develops a target setting methodology for the (Moving Ahead for Progress) MAP-21 Interstate Travel Time Reliability Measure of “Percent of the Person-Miles Traveled on the Interstate that are Reliable” (PMTR-IS). The study uses data specific to Virginia for a set of independent variables (Hourly Volume, and Volume/Capacity Ratio, Truck Percentage, Equivalent Property Damage Only Rate, Lane Impacting Incident Rate, Number of Lanes, Presence of Safety Service Patrol, Terrain, Urban Designation) to predict whether a MAP-21 reporting segment is reliable. This is used to estimate predicted PMTR-IS with the MAP-21 specified formula. CART (classification and regression trees) models were used with 1,536 different configurations. Of the four best performing models, one was selected using engineering judgement. Datasets from 2017 and 2018 were used for training, and from 2019 and 2020 for testing. The predicted and calculated PMTR-IS were compared, and the error percentage was lower than 1% for 2019, which can be considered negligible. The 2020 error rate was higher and can perhaps be attributed to unusual reliability because of the impacts of the pandemic on travel. Sensitivity analyses revealed that the predicted PMTR-IS is reactive to capacity increase in unreliable sections at a local level, slower to respond to local AADT (annual average daily traffic) increase, and stable to small statewide AADT oscillations. The authors recommend using this methodology for target setting, taking into consideration the state authorities’ other strategic priorities.

MEASUREMENT OF DELAY USING TRAVEL TIME RELIABILITY STATISTICS IN AN URBAN OUTER CORRIDOR

Unexpected delay on freeways is the prime cause of dissatisfaction in road users. Increasing traffic, adverse environmental conditions, accidents, time, season, location and many more factors influence travel time and cause delay. There is no direct method to estimate delay. It is calculated from trip time estimates. Thus, it is a very big challenge for transportation professionals to develop a model that accurately estimates the trip time for a trip at a particular time, by a specific mode of transport. Subsequently, the reliability of the delay calculated from those trip time estimates is often doubtful. Further, the measurement of delay using the trip time data is another big thing. This paper is a step toward measuring the delay in an accurate way using travel time reliability measures. The study was conducted on the two modes of public transportation (City bus and Auto) in an urban corridor of length 16.3 km, in Hyderabad city, India. In this study, a license plate survey was conducted for data collection, travel time-based statistical analysis was employed for estimation of trip time and by making use of travel time measures, the delay was measured. The approach was validated graphically to portray its accuracy.

Which Aggregation Fits Best? The Use of Linear Regression to Show the Influence of Temporal and Spatial Aggregation of Sparse Probe Vehicle Data on the Explanation of Travel Time Reliability

Aggregation of sparse probe vehicle data (PVD) is a crucial issue in travel time reliability (TTR) analysis. This study, therefore, examines the effect of temporal and spatial aggregation of sparse PVD on the results of a linear regression analysis where two different measures of TTR are analyzed as the dependent variable. Our results show that by aggregating the data to longer time intervals and coarser spatial units the linear model can explain a higher proportion of the variance in TTR. Furthermore, we find that the effects of road design characteristics in particular depend on the variable used to represent TTR. We conclude that the temporal and spatial aggregation of sparse PVD affects the results of linear regression explaining TTR.

Modelling road traffic congestion at urban merge section under mixed traffic conditions

Identification and evaluation of traffic congestion are critical for better management of urban traffic, especially at merge sections where two different traffic streams converge and generate shockwaves. This study evaluates the travel time reliability measure (travel time index (TTI)) as a reliable congestion measure by analysing the impact of traffic flow parameters such as traffic volume, capacity, road geometrics and conflicting traffic flows at merge sections on TTI under mixed traffic conditions. The data required for congestion analysis were collected through video recording. Statistical models were developed to: (a) evaluate the relationship between road geometric characteristics and capacity of merge sections; (b) estimate traffic density and its evolution with time and compare it against occupancy values; and (c) correlate the relationship between TTI and traffic behaviour at merge sections and determine the variations in TTI for different levels of service of merge sections. The models developed are statistically validated using secondary data sets from the same study section, as well as from data collected from other multi-lane merge sections. The developed model successfully estimated recurring congestion by considering both space and time. This study highlights the potential usage of TTIs in capturing travel time variations more accurately at merge sections under mixed traffic conditions.

Analyzing Travel Time Reliability from Sparse Probe Vehicle Data: A Case Study on the Effects of Spatial and Temporal Aggregation

Travel time reliability (TTR) indices have gained considerable attention for evaluating the quality of traffic infrastructure. Whereas TTR measures have been widely explored using data from stationary sensors with high penetration rates, there is a lack of research on calculating TTR from mobile sensors such as probe vehicle data (PVD) which is characterized by low penetration rates. PVD is a relevant data source for analyzing non-highway routes, as they are often not sufficiently covered by stationary sensors. The paper presents a methodology for analyzing TTR on (sub-)urban and rural routes with sparse PVD as the only data source that could be used by road authorities or traffic planners. Especially in the case of sparse data, spatial and temporal aggregations could have great impact, which are investigated on two levels: first, the width of time of day (TOD) intervals and second, the length of road segments. The spatial and temporal aggregation effects on travel time index (TTI) as prominent TTR measure are analyzed within an exemplary case study including three different routes. TTI patterns are calculated from data of one year grouped by different days-of-week (DOW) groups and the TOD. The case study shows that using well-chosen temporal and spatial aggregations, even with sparse PVD, an in-depth analysis of traffic patterns is possible.

Measures of Functional Reliability of Two-Lane Highways

Rural two-lane highways are the most common road type both in Poland and globally. In terms of kilometres, their length is by far greater than that of motorways and expressways. They are roads of one carriageway for each direction, which makes the overtaking of slower vehicles possible only when there is a gap in the stream of traffic moving from the opposite direction. Motorways and express roads are dual carriageways that are expected to support high speed travel mainly over long distances. Express roads have somewhat lower technical parameters and a lower speed limit than motorways. Two-lane highways are used for both short- and long-distance travel. The paper presents selected studies conducted in Poland in 2016–2018 on rural two-lane highways and focuses on the context of the need for their reliability. The research was carried out on selected short and longer road sections located in various surroundings, grouped in terms of curvature change rate CCR, longitudinal slopes and cross-sections (width of lanes and shoulders). The studies of traffic volumes, travel time and travel speed, as well as traffic density, will be used to analyze traffic performance and identify measures of travel time reliability. The analyzed roads were characterized by good technical parameters and significant variability of traffic volume throughout the day, week and year. Some roads experience congestion, i.e., situations in which traffic volume Q is close to or above respective road capacity C. In order to determine the form of the suitable reliability measures, it will be important to determine the extent to which a road’s geometric and traffic characteristics impact travel speed and time. The paper presents well-known reliability measures for dual carriageways and proposes new measures, along with an evaluation of their usefulness in the assessment of the functioning of two-lane highways.

Estimating Freeway Level-of-Service Using Crowdsourced Data

In traffic operations, the aim of transportation agencies and researchers is typically to reduce congestion and improve safety. To attain these goals, agencies need continuous and accurate information about the traffic situation. Level-of-Service (LOS) is a beneficial index of traffic operations used to monitor freeways. The Highway Capacity Manual (HCM) provides analytical methods to assess LOS based on traffic density and highway characteristics. Generally, obtaining reliable density data on every road in large networks using traditional fixed location sensors and cameras is expensive and otherwise unrealistic. Traditional intelligent transportation system facilities are typically limited to major urban areas in different states. Crowdsourced data are an emerging, low-cost solution that can potentially improve safety and operations. This study incorporates crowdsourced data provided by Waze to propose an algorithm for LOS assessment on an hourly basis. The proposed algorithm exploits various features from big data (crowdsourced Waze user alerts and speed/travel time variation) to perform LOS classification using machine learning models. Three categories of model inputs are introduced: Basic statistical measures of speed; travel time reliability measures; and the number of hourly Waze alerts. Data collected from fixed location sensors were used to calculate ground truth LOS. The results reveal that using Waze crowdsourced alerts can improve the LOS estimation accuracy by about 10% (accuracy = 0.93, Kappa = 0.83). The proposed method was also tested and confirmed by using data from after coronavirus disease 2019 (COVID-19) with severe traffic breakdown due to a stay-at-home policy. The proposed method is extendible for freeways in other locations. The results of this research provide transportation agencies with a LOS method based on crowdsourced data on different freeway segments, regardless of the availability of traditional fixed location sensors.

Development of congestion index model and analysis of mitigation measures on urban arterials using microsimulation

Traffic congestion is one of the main issues related to traffic engineering, planning, and policies that directly influence the economy, environment, and lifestyle in developing countries, particularly in India. This study's main objectives are to establish the congestion index model and analyze the various mitigation measures using microsimulation. The required data were collected from selected two corridors in Tiruchirappalli, India. A correlation test was performed to identify the significant parameters that influence traffic congestion. Congestion Indices and Travel time reliability measures were computed to quantify congestion. Using a conventional regression technique, the congestion index model was developed to predict the congestion levels. The validated model predicts the congestion accurately. The mentioned statistical tests and model development were performed in SPSS 25.0 software at 95% confidence interval. A VISSIM based microsimulation was calibrated and simulated various mitigation measures with possible scenarios. Thus, the mind-numbing traffic jams can be reduced, and as a result, a great loss for the Indian economy can be reduced.

Exploring Correlations between Travel Time Based Measures by Year, Day-of-the-week, Time-of-the-day, Week-of-the-Year and the Posted Speed Limit

ABSTRACT This research explores correlations between travel time-based measures, comprehensively, by year, day-of-the-week (DoW), time-of-the-day (ToD), week-of-the-year (WoY), and the posted speed limit using raw one-minute interval travel time data for 3,290 road links in Charlotte, North Carolina. The data were processed, normalized, and categorized into 57 datasets accounting for the year, DoW, ToD, WoY, and the posted speed limit. Pearson correlation coefficient matrices were generated to explore the correlations between travel time measures (minimum, maximum, and average; 10th, 15th, 50th, 85th, 90th, and 95th percentile travel times), travel time reliability measures (buffer time – BT, buffer time index – BTI, planning time index – PTI, and travel time index – TTI), and travel time variability measures (based on 90th, 85th, 15th and 10th percentile travel times). The percentages based on scores associated with the correlations were also examined. Travel time measures and travel time variability measures are moderately or highly correlated with each other. BT is moderately or highly correlated with all other measures, while the correlation between BTI, PTI and TTI and travel time measures is relatively low. There is a positive correlation between BTI and PTI and a negative correlation between BTI and TTI in most instances.

Analysis of Traffic Congestion Impacts of Urban Road Network under Indian Condition

Traffic congestion has been an area of major bother across the globe. The existing infrastructure is not able to cope with the new traffic demand. Furthermore the restriction of the space and outside activities influencing the traffic congestion. The emerging country like India, where the traffic conditions comprise heterogeneous traffic with no lane discipline, further creates more complicated scenarios for the researcher. A substantial portion of working hours is getting wasted on the roads because of traffic congestion, which imposes the negative effect on the overall economy. There has been numerous of literature and studies for analysis of the traffic congestion and its impacts. However, the result has not been much satisfying. In the present study congestion forecast is aimed under mixed traffic with no lane discipline towards identifying the inherent viability of the diversified traffic situation and presents better recommendations in controlling and evading these prolonged traffic jams. The urban highway systems were considered as a study area. Required particulars were collected by a License Plate Matching method using video graphic survey for the day rush timing considered from 8:00 AM to 10:00 AM and off peak hours to estimate the travel time of a distinct class of motorized vehicles for selected sections of the urban roadway. Congestion indices for both the up and down traffic of a particular road were evaluated from the data collected from the video recording. Traffic congestion impacts were analyzed and possible mitigation measures have been suggested. The study mainly focused on traffic jam indices with regarded to travel time reliability measures to observe the functional effectiveness of the urban road network.

Quantifying the Mobility Benefits of Express Lanes using Real-Time Traffic Data

Traffic congestion is one of the major problems facing transportation agencies, especially in urban areas. Agencies are exploring ways to use the existing transportation infrastructure efficiently by deploying appropriate traffic management strategies. One of these strategies is the use of express lanes, which are expected to effectively mitigate congestion and increase the reliability of highway facilities. Express lanes are managed toll lanes, separated from general-purpose lanes within a freeway facility. The goal of this study was to quantify the mobility benefits of express lanes by comparing the performance of express lanes with that of their adjacent general-purpose lanes, and by assessing the performance of the general-purpose lanes when the express lanes were open versus when the express lanes were closed. The Buffer Index (BI), a travel time reliability measure, was selected as the performance measure. The analysis was based on 95Express, express lanes along I-95 in Miami, Florida. Overall, the results indicated that BIs for the express lanes were significantly lower than the BIs for the general-purpose lanes, and the BIs for the general-purpose lanes were significantly lower when the express lanes were open compared with the periods when the express lanes were closed. The study results showed mobility improvements on both the express lanes and the general-purpose lanes, although the extent of the improvements varied by direction (i.e., northbound and southbound) and time of day (i.e., a.m. peak, p.m. peak, daytime off-peak, and nighttime off-peak). Transportation agencies may use these findings to quantify and evaluate the mobility benefits of the express lanes and the general-purpose lanes on express lane facilities.

Performance Evaluation of Urban Arterial Network Using Wi-Fi Sensors Under Heterogeneous Traffic Conditions

Traffic data collection is one of the major issue faced by the researches as well as government authorities for the study of traffic movement behaviour. Conventional methods of data collection under mixed traffic condition do not yield promising results and cannot be completely relied upon. Thus, this study aims to assess the potential use of Wi-Fi based sensors for the performance evaluation of arterial closed network and formation of O-D matrix in Centre Business District area of Surat city with help of Wi-Fi based data collection technique. This paper expounds the essentiality for the use of Wi-Fi data as an outlining parameter in the performance evaluation of the study network, which is unlike the conventional examinations that signifies the use of Bluetooth observations for evaluating the network operation based on the travel time reliability measures. The data provides exceptional results than the other hackneyed techniques, which results in less sample size and accuracy. Further, the network development was executed with the deployment of four sensors at critical confluences. This was useful for scrutinizing the traffic movements along the arterial network.The sensors used here work on the principle of recurring detections of MAC ids for the devices along with their analogous time stamps for the duration in which they remain within the detection range. Thus, re-identification of the same moving vehicle along various network points will help to determine the travel time where the difference in the time stamps of certain MAC ids moving among several junction points were exercised for the determination of travel time and development of O-D matrix along all possible routes. The Wi-Fi data obtained from the sensors in Surat city network corresponds to the critical routes for various hours of the day. This leads to development of primary and secondary matrix comprising of total and cut through traffic, which are useful for multiple aspects. The trial results show the successful penetration (unique detections to vehicles) ratio ranging from 20-to30percentage after significant results from various statistical analysis test as well as considerations from literature studies. The ultimate outcomes will justify the substantiation of results obtained from sensors along with assessments with other performance-measuring device.

Effect of toll roads on travel time reliability within its vicinity: a case study from the state of North Carolina

ABSTRACT This research is carried out to evaluate the efficacy of toll roads in reducing travel time and improving travel time reliability on links within its vicinity, using data for the Triangle Expressway in Raleigh, North Carolina, United States. Various travel time reliability measures were computed for each link, referred to as Traffic Message Channel (TMC), by day-of-the-week and time-of-the-day. The travel time reliability of the links adjacent to the toll roads (both the parallel route and the cross streets) before construction, during phase 1 operation, during phase 2 operation, and after opening the toll road for complete service was examined. The statistical tests reveal a notable improvement in travel time reliability measures within the vicinity of the toll road after opening the toll road for complete service. The methodology and study results help transportation planners and engineers in comparing the performance or efficiency of toll roads/managed lane facilities in terms of travel time reliability and enhancing the sustainability of the transportation system.