Freeway Corridor Research Articles

Dynamic Distance-Based Pricing Scheme for High-Occupancy-Toll Lanes Along a Freeway Corridor

Dynamic Distance-Based Pricing Scheme for High-Occupancy-Toll Lanes Along a Freeway Corridor

Using frequency domain analysis to elucidate travel time reliability along congested freeway corridors

Travel time reliability (TTR) is an essential measure of service for traffic performance management, especially for congested freeway corridors. This paper proposes a systematic analytical framework to analyze the predictable and unpredictable variations in route TTR in corridor networks. More specifically, the predictable variation in route TTR is estimated with a deterministic fluid-based polynomial arrival queue (PAQ) model, while the unpredictable variation in route TTR is analyzed through the residual induced by the PAQ estimation model. Based on the output of the PAQ estimation model, a frequency-domain approach is proposed to decompose the observed time-domain travel time into underlying unobserved factors in different temporal resolutions. With the discrete-time Fourier transform method and the Butterworth low-pass filtering technique, it is capable of analytically uncovering different temporal scales of predictable variations in TTR, including the trend of day-to-day variations, the dynamics of within-day variations, as well as the stochasticity of within-period variations. Connecting with the unpredictable variations represented by the residual due to the PAQ approximation model, we can comprehensively elucidate TTR for congested freeway corridors. Case studies are conducted to show the effectiveness of the proposed frequency-domain approach in elucidating and measuring different contributing elements in TTR, and more specifically, the four components, i.e., the trend of day-to-day variations, the dynamics of within-day variations, the stochasticity of within-period variations, and the residual induced by the PAQ estimation model, account for 64.9 %, 20.2 %, 1.2 %, 13.7 % of variations in the route travel time, respectively, based on a case study with the PeMS dataset.

Physics-informed neural networks for integrated traffic state and queue profile estimation: A differentiable programming approach on layered computational graphs

This paper presents an integrated framework for physics-informed joint traffic state and queue profile estimation (JSQE) on freeway corridors, utilizing heterogeneous data sources. The integrated modeling framework aims to maximize the benefits of information from physics-informed analytical traffic flow models and field observations while leveraging joint estimation spaces. Potential inconsistencies between different modeling components must be acknowledged and carefully managed to ensure model feasibility. To minimize such inconsistencies, a nonlinear programming model is formulated for the JSQE problem, taking into account traffic flow models and observations from both corridor and local segment levels. At the corridor level, a fluid queue approximation is employed to model queuing dynamics. Assuming polynomial arrival and departure rates, critical system variables such as time-dependent delay, travel time, and queue length are analytically derived. To preserve the differentiability of traffic state variables, continuous space–time distribution functions are introduced to model traffic flow variables and partial differential equations. A computational graph is constructed to represent the nonlinear programming model in a layered structure, which is then solved using a forward–backward method. Comprehensive numerical experiments based on real-world and hypothetical datasets are designed to demonstrate the effectiveness of the proposed framework.

Optimal Deployment of Sensors along Freeway Corridors for Traffic Accident Detection

Sensor deployment is important to the detection performance of traffic accidents on freeways. However, the temporally and spatially varying effects of accident risks are seldom considered in sensor deployment, which can induce a decrease in accident detection accuracy and detection timeliness. To address this problem, this paper proposes an optimal deployment method of sensors according to the temporal-spatial effect of accidents on traffic flow. Considering the uncertainty of traffic accidents, the temporal-spatial distribution of accident risk is estimated according to historical accident data. Then the kinematic wave model and Van Aerder model are introduced to analyze the temporal-spatial effect of accidents on traffic flow. To obtain the optimal locations of sensors, the sensor deployment problem is converted into a coverage problem on accident risk in time and space, and an optimal deployment model of sensors is built by considering the temporal-spatial effect of accident risk, the coverage rate on accident risk, the detection timeliness of the accident, and deployment cost. The particle swarm optimization algorithm is introduced to solve it. A case study is carried out to validate the proposed method using consistency, sensitivity, reliability, and comparison experiments. The results show that the proposed method is effective and reliable under different traffic volumes, and it outperforms the uniform spacing method in traffic accident detection.

A simulation study of cooperative and autonomous vehicles (CAV) considering courtesy, ethics, and fairness.

Autonomous vehicles (AV) can be programmed to act cooperatively. Previous research on cooperative and autonomous vehicles (CAV) suggests they can substantially improve traffic system operations in terms of mobility and safety. However, these studies do not explicitly take each vehicle's potential gain/loss into consideration and ignore their individual levels of willingness to cooperate. They do not account for ethics and fairness either. In this study, several cooperation/courtesy strategies are proposed to address the above issues. These strategies are grouped into two categories based on non-instrumental and instrumental principles. Non-instrumental strategies make courtesy/cooperation decisions based on some courtesy proxies and a user-specified courtesy level, while instrumental strategies are based only on courtesy proxies related to local traffic performance. Also, a new CAV behavior modeling framework is proposed based on our previous work on cooperative car-following and merging (CCM) control. With such a framework, the proposed courtesy strategies can be easily implemented. The proposed framework and courtesy strategies are coded in SUMO microscopic traffic simulator. They are evaluated considering different levels of traffic demand on a freeway corridor consisting of a work zone and three weaving areas of different types. Interesting findings are drawn from the simulation results, one of which is that the instrumental Local Utilitarianism strategy performs the best in terms of mobility, safety, and fairness. In the future, auction-based strategies can be considered to model how CAV make decisions.

Simulation-based dynamic origin–destination matrix estimation on freeways: A Bayesian optimization approach

This study focuses on dynamic origin–destination demand estimation problem on freeway networks. Existing studies on this problem rely on high-coverage of traffic measurements and assumptions on travel times, exhibiting limitations in real-world applications. We formulate the problem as a bi-level programming model, where micro-simulations are incorporated to precisely model traffic flows/travel times on freeways. The bi-level programming model cannot provide explicit closed-form expressions for the objective function and its derivatives, and also intrinsically high-dimensional. Thus, it is highly challenging to find efficient solution algorithms. In this regard, a problem-specific and computationally efficient Bayesian optimization approach is designed. Herein, a novel surrogate model is proposed by embedding a physical surrogate model (it characterizes underlying physical mechanisms and provides global yet less precise approximations) into a functional surrogate model (it provides precise local approximations). The embedding provides problem-specific knowledge for the surrogate model. More importantly, it also restricts the feasible region, enabling the surrogate model to efficiently deal with high-dimensional problems. Gaussian process can be served as the functional surrogate model. Two linear physical surrogate models are proposed to capture interactions between travel demand and traffic measurements. To deal with constraints in the surrogate model, a projection-distance based acquisition function is designed. In searching for new points, the proposed acquisition function is capable of assigning unique weight of exploration to each feasible solution. The proposed approach is validated based on a freeway corridor example, which indicates its outperformance over existing dynamic origin–destination estimation methods in terms of computational efficiency and solution accuracy.

Integrated Real-Time Interchange Control System for Freeway Ramps and the Connecting Arterial

Contending with various recurrent congestion patterns on major freeway corridors has long been a prioritized task of the transportation community. Most of these patterns take place on a freeway interchange’s on-ramp and off-ramp segments where both exiting and merging flows often trigger a series of lane changes, resulting in significant freeway speed reduction and propagation of traffic queues to neighboring surface streets. To concurrently mitigate freeway traffic congestion and minimize any negative impacts on the nearby local arterials, this study has developed an Integrated Real-time Interchange Control (IRIC) system, which maximizes the benefits of both freeway and arterial users by fusing an integrated off-ramp signal control (IOSC) module, an off-ramp queue impact (OQI) model, a lane group-based (LGB) traffic model, and a ramp metering control strategy as well as arterial signal optimization modules. The proposed system with both ramp metering functions and local arterial signal design capabilities can determine whether to implement system-wide optimization or merely the ramp metering based on real-time detected traffic conditions. The evaluation results from experiments have confirmed the effectiveness of the developed real-time interchange control system, especially with respect to its ability of preventing ramp queue overflows to the neighboring arterials and minimizing the off-ramp spillback onto the freeway mainline.

Reliable Traffic State Identification Using High-Resolution Data: A Consistent Offline-Online Dynamic Time Warping–Based Time Series Clustering Approach

Reliable real-time traffic state identification (TSI) provides key support for traffic management and control. Although substantial efforts have been devoted to TSI, considering the high dynamics and stochasticity of traffic flows, there remain challenges in providing reliable and consistent TSI results, especially in online network–level applications. In this study, we propose a time series clustering-based offline-online modeling framework for reliable TSI using high-resolution traffic data. Specifically, in the proposed framework, the offline module extracts representative traffic state patterns from massive historical data, which serve as the state references in the online module when performing real-time TSI with streaming information. Instead of point data, the proposed framework uses high-resolution traffic data in the form of time series, providing rich information on traffic flows and details on their short-term fluctuations and stable long-term trends. In the offline module, considering the fuzziness of traffic states, we introduce a fuzzy c-means based clustering method for offline traffic flow series clustering and traffic state pattern extraction, within which the dynamic time warping algorithm is adopted for measuring the similarity between different time series, and the optimal number of clusters is determined by a proposed critical segment–based method to reach consistent TSI in network-wide applications. In the online module, a dynamic programming–based real-time TSI approach is developed to produce reliable and smooth identification results. Extensive numerical experiments on a 20-mi-long freeway corridor in California, USA, were performed to validate the proposed framework. Results demonstrate the effectiveness of the proposed framework.

Comparative Analysis of AERMOD-Predicted Pollutant Concentrations by Input Source Type in Atlanta, GA

This research investigates the differences in AERMOD-predicted pollutant concentrations across input source types (AREAPOLY, VOLUME, LINE, RLINE, and RLINEXT without barriers). AERMOD (version 19191) microscale dispersion modeling is performed for the Atlanta I-75/I-575 Northwest Corridor (NWC) subarea in the U.S., including freeway corridors, managed lanes, connecting arterials, and intersections serving the NWC system. Modeling is performed for all link-receptor pairs, for each hour of the year, using the approved AERMET meteorological files (24 h × 365 days in 2019) from Georgia Environmental Protection Division (GA EPD), and emissions rates are estimated using MOVES-Matrix, based on predicted speed and volume by hour (output from ARC’s ABM2020 travel demand model). The AERMOD comparative analyses are performed for the same receptor sets, link-level volumes and speeds, and fleet compositions, across the five source types to generate comparative model results by input source type for the subarea. Results indicate concentrations from RLINEXT and RLINE were similar, with differences smaller than 0.05%, and their results were significantly higher than other source types. The results from AREAPOLY were similar to those from LINE (both smaller than RLINE and RLINEXT results). It is indicated that the results from VOLUME are systematically different from AREAPOLY and LINE for both downwind (much lower than AREAPOLY and LINE) and upwind (much higher than AREAPOLY and LINE) of the sources, and the sensitivity analysis indicates these predictions occur when wind speeds are low (less than approximately 2.5 m/s), which is because the enhanced wind meander approach employed with VOLUME is different from other source types.

Integrated Traffic Control for Freeway Recurrent Bottleneck Based on Deep Reinforcement Learning

Recent advances in deep reinforcement learning have shown promising results in solving sophisticated control problems with high dimensional states and action space. Inspired by this, we use the latest deep reinforcement learning (DRL) methods to improve freeway traffic mobility and alleviate recurring bottlenecks and congestion. More specifically, this paper proposes a centralized traffic control system that can coordinate multiple ramp metering (RM) and variable speed limit (VSL) traffic controllers on freeways to minimize the total travel time. The system uses a novel double-layer structure to synchronize different traffic controllers and introduces the actor-critic-based DRL methods to learn joint actions in a high-dimensional traffic environment. The reward function takes into account the waiting time of vehicles, the average speed of different road sections, and the on-ramp queuing limit to improve traffic mobility. We also proposed an integrated feedback controller as a benchmark. The simulation results show that the actor-critic-based methods are superior to other methods and can save more than 20% of the total travel time. We also analyzed the curse of dimensionality problem by comparing the performance of two scenarios in the simulation: one is a single-ramp interweaving area scenario; the other is a large freeway corridor with multiple on-ramps and off-ramps. The results show that our system can effectively handle these two situations without significant performance degradation, which means that the centralized control system can effectively control freeway corridors by directly guiding various traffic controllers. This also leads to the conclusion that we can use a centralized actor-critic-based control unit to manage medium-scale freeway traffic to save computing resources instead of using complex collaboration strategies.

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.

High Volume Freeway Travel Time Reliability and the COVID-19 Pandemic

This paper examines the effect of the COVID-19 pandemic on travel time reliability (TTR) performance on two high traffic volume freeway corridors (I-90 and I-405) in the U.S.A. Specifically, the travel time distributions (TTDs) during and before the pandemic are compared. The paper also examines which TTR metrics best capture the effect of the pandemic on reliability performance. There were statistically significant differences, at the 95% confidence level, between TTD in 2020 and corresponding TTDs in 2018 and 2019. Not surprisingly, all measures of central tendency and all measures of dispersions were reduced during the pandemic. Consequently, it was concluded that TTR performance improved during the pandemic regardless of what TTR definition was used. Travel time index (TTI), planning time index (PTI), and the level of travel time reliability (LOTTR) metrics improved during the pandemic, albeit at different rates. In contrast, the buffer index and coefficient of variation increased. In other words, whether an analyst would identify that TTR improved or decreased during the pandemic, and by how much, would be a function of which TTR metric was applied. Not surprisingly, the more congested the roadway section, the greater the impact the pandemic-related interventions had on TTR. It was found that, practically speaking, there are no differences in TTI, PTI, or LOTTR values when TTDs are formulated using a 5 min or 15 min aggregation interval. It was concluded that analysts need to have a deep understanding of the underlying TTD and the various TTR definitions when evaluating changes in freeway systems’ TTR.

Quantitative Evaluation of the Impacts of the Time Headway of Adaptive Cruise Control Systems on Congested Urban Freeways Using Different Car Following Models and Early Control Results

The impact of driving automation and adaptive cruise control (ACC) on traffic performance has been increasingly studied in recent years. This paper focuses on two widely used ACC car following models and investigates the impact of the time headway parameter on traffic operation and performance on one of the busiest freeway corridors in Ontario, Canada. Using Aimsun microsimulation, we compare two commonly used ACC car following models; the intelligent driver model (IDM) and Shladover’s model which has been recently adopted in Aimsun Next 20. Several experiments have been conducted to evaluate the freeway performance for different desired headway settings and market penetration rates of ACC-equipped vehicles. Simulations results confirm the reported IDM drawbacks of having a slow response leading to headway errors which are less pronounced with Shladover’s model thereby leading to more accurate quantification by the latter. This study further presents a simple on-off ACC-based traffic control strategy which aims to adapt in real time the driving behavior of ACC-equipped vehicles to the prevailing traffic conditions so that freeway performance is improved. The simulation results demonstrate that, even for low penetration rates of ACC vehicles, the proposed control concept improves the average network throughput, delay, and speed compared to the case of only manually driven or uncontrolled ACC vehicles.

Evaluating connected vehicle-based weather responsive management strategies using weather-sensitive microscopic simulation

The purpose of this study is to perform analysis, modeling, and simulation (AMS) to investigate the effectiveness of connected vehicle (CV)-based Weather Responsive Management Strategies (WRMS) to address safety concerns on freeway corridors under adverse weather conditions. This study investigates three CV-based WRMS applications: Forward Collision Warning (FCW), Early Lane Change (ELC) advisory, and Variable Speed Limit (VSL), designs operational alternatives for WRMS using CV data, and develops an AMS tool using a weather-sensitive microscopic traffic simulator to understand the effectiveness of the three WRMS under different scenarios. Various CV market penetration rates (MPR), weather conditions, and WRMS algorithm settings are tested in this study. The case study is based on a real-world freeway corridor, a segment of the I–80 Connected Vehicle Testbed in Wyoming. The simulation results show the effectiveness of selected WRMS applications and provide operational insights that state and local transportation agencies may use in future strategic planning and operations of their weather-responsive programs.

Travel time forecasting on a freeway corridor: a dynamic information fusion model based on the random forests approach

Purpose Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways. Design/methodology/approach As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways. Findings The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions. Originality/value This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

Statistical Distance-Based Travel-Time Reliability Measurement for Freeway Bottleneck Identification and Ranking

Freeway bottleneck identification is an essential component in the process of deploying mitigation strategies to reduce congestion at freeway bottlenecks. Most previous studies on bottleneck identification focus on recurrent bottlenecks, and limited work has been conducted to identify the locations of non-recurrent bottlenecks. Therefore, in this study, we propose a new travel time reliability (TTR) measurement and develop a freeway bottleneck identification method based on this measurement, which can identify with high probability not only recurrent bottlenecks but also the locations of non-recurrent bottlenecks. The TTR measurement is developed based on statistical distance between travel time distributions. Three statistical distance measurements, Jensen–Shannon divergence, Wasserstein distance, and Hellinger distance, are applied in the TTR measurement. The bottleneck identification method is evaluated in a case study on I-15 freeway corridor in Salt Lake City, Utah. The three statistical distance measurements show good consistency in ranking locations by the impacts of recurrent and non-recurrent congestion, especially for extreme cases with very high or low variation between travel time distributions. The recurrent bottlenecks identified in this study show their clustering characteristics, which is similar to the generating and dismissing process of recurrent congestion. The locations with high probability of non-recurrent bottlenecks scatter both spatially and temporally, which agrees with the random characteristic of non-recurrent congestion.

Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses

Due to the increasing traffic volume in metropolitan areas, short-term travel time prediction (TTP) can be an important and useful tool for both travelers and traffic management. Accurate and reliable short-term travel time prediction can greatly help vehicle routing and congestion mitigation. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm using the available data. In this study, the travel time data was provided and collected from the Regional Integrated Transportation Information System (RITIS). Then, the travel times were predicted for short horizons (ranging from 15 to 60 min) on the selected freeway corridors by applying four different machine learning algorithms, which are Decision Trees (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory neural network (LSTM). Many spatial and temporal characteristics that may affect travel time were used when developing the models. The performance of prediction accuracy and reliability are compared. Numerical results suggest that RF can achieve a better prediction performance result than any of the other methods not only in accuracy but also with stability.

Evaluating the effect of ramp metering on freeway safety using real-time traffic data

Ramp metering relieves traffic congestion, reduces delay, and maintains the capacity flow on freeways. Due to its operational mechanism, ramp metering can also improve freeway safety. While the operational benefits of ramp metering have extensively been quantified, research on its safety effects is sparse. This study focused on evaluating the effects of ramp metering on the safety performance of the freeway mainline. It developed a crash risk prediction model for segments downstream of the entrance ramps when ramp metering is activated. The study was based on a corridor with system-wide ramp metering along I-95 in Miami, Florida. Real-time traffic, crash, and ramp metering operations data collected from 2016 to 2018 were used in the analysis. The study adopted a matched crash and non-crash case approach to evaluate the crash risk when ramp meters were activated and deactivated. A penalized logistic regression model was developed using a bootstrap resampling technique to estimate the effects of ramp metering activation and select important variables that could predict crash risk when ramp meters were activated. Results indicated that ramp metering improves safety along the freeway corridor by reducing the crash risk downstream of the entrance ramps. During ramp metering activation, the crash risk on segments downstream of the entrance ramps 5 min later can be predicted using the difference in the average lane speeds between upstream and downstream detectors, the average traffic volume in the lanes at the downstream and upstream detectors, and the coefficient of variation of speed between lanes in the upstream detectors. Also, the coefficient of variation of occupancy downstream could predict the crash risk 15 min later. The study results could be used by transportation agencies when evaluating the deployment of ramp meters. Moreover, the developed crash risk prediction model could be used in real-time to help agencies identify the increased crash risk and provide appropriate warning information to the upstream traffic.

Assessing the Impact of Automated and Connected Automated Vehicles on Virginia Freeways

This study assesses the impact of the introduction of connected and automated vehicles on Virginia freeway corridors. Three vehicle types: legacy vehicles (LV), automated vehicles (AV), and connected automated vehicles (CAV), were considered in mixed traffic scenarios. Previous relevant studies were reviewed and the proper operating parameters for LV, AV, and CAV identified. AV and CAV driving behavior models were developed in the VISSIM environment. According to the basic freeway test network results, AV and CAV increase road capacity by 29% and 91%. In the merging freeway test network, AV and CAV increase road capacity by 48% and 60% compared with LV, respectively. A model with diverse LV, AV, and CAV market penetration and diverse traffic demand was tested on I-95 in Virginia, where the research team tested the speed and throughput. Under the current traffic demand, the average speed was higher when there were more AV and no CAV in the traffic flow. However, the average speed of CAV in a congested segment is higher than LV. In the case of throughput, CAV shows poor performance under current traffic demand. With increased traffic demand, high penetrations of AV and CAV present better performance because of their short headway and homogeneity. Therefore, the study predicts that in the future, as the traffic demand grows, AV and CAV can reduce traffic congestion.

A Feature-Based Approach to Large-Scale Freeway Congestion Detection Using Full Cellular Activity Data

Most existing cellular probe-based freeway congestion detection methods rely on on-call WLT (Wireless Location Technologies) signal transition data. However, these techniques facing difficulties such as small sample size, frequent road tests, safety, and privacy issues. This article presents a novel approach using the FCA data for traffic congestion detection on freeways. Two cellular activity features, the link pseudo speed and link probe activity, are defined and calculated. A rule-based algorithm is then developed to determine the traffic congestion state. The proposed method has been implemented and a prototype system has been deployed for a major freeway corridor in China. Validated by fixed-point detector data and incident records, the proposed method is able to identify real-time freeway traffic congestion accurately.