Highway Traffic Conditions Research Articles

Traffic Volume Forecast of Parking Lot Entrance and Exit Roads

As people's living standards improve, commercial plaza development also more and more quickly, commercial plaza and the surrounding areas of transport vehicles, in the parking lot entrances and exits of highway road traffic conditions, the parking lot entrances and exits of low traffic capacity and traffic organization chaos (motor vehicles, electric vehicles and the status of the bicycle traffic complex). It is necessary to predict the traffic volume of the entrance and exit roads of the parking lot. In ningyang county lingyun building as an example, through direct observation records investigation parking lot parking condition and facilities, analysis of traffic organization form of the parking lot entrances, investigate the status of the parking lot entrance road and surrounding roads, using artificial paper counting method to collect parking entrance road traffic flow, speed and other data, The time series moving average and exponential smoothing are used to predict the traffic volume at the entrance and exit of the parking lot. The prediction results show that the two methods are suitable for the parking lot entrance traffic volume prediction, and the moving average method is more accurate than the exponential smoothing method, which is closer to the actual value.

A Hybrid Deep Reinforcement Learning and Optimal Control Architecture for Autonomous Highway Driving

Autonomous vehicles in highway driving scenarios are expected to become a reality in the next few years. Decision-making and motion planning algorithms, which allow autonomous vehicles to predict and tackle unpredictable road traffic situations, play a crucial role. Indeed, finding the optimal driving decision in all the different driving scenarios is a challenging task due to the large and complex variability of highway traffic scenarios. In this context, the aim of this work is to design an effective hybrid two-layer path planning architecture that, by exploiting the powerful tools offered by the emerging Deep Reinforcement Learning (DRL) in combination with model-based approaches, lets the autonomous vehicles properly behave in different highway traffic conditions and, accordingly, to determine the lateral and longitudinal control commands. Specifically, the DRL-based high-level planner is responsible for training the vehicle to choose tactical behaviors according to the surrounding environment, while the low-level control converts these choices into the lateral and longitudinal vehicle control actions to be imposed through an optimization problem based on Nonlinear Model Predictive Control (NMPC) approach, thus enforcing continuous constraints. The effectiveness of the proposed hierarchical architecture is hence evaluated via an integrated vehicular platform that combines the MATLAB environment with the SUMO (Simulation of Urban MObility) traffic simulator. The exhaustive simulation analysis, carried out on different non-trivial highway traffic scenarios, confirms the capability of the proposed strategy in driving the autonomous vehicles in different traffic scenarios.

GPS-Based Traffic Conditions Classification Using Machine Learning Approaches

This paper addresses the problem of accurately estimating traffic conditions based on sparse GPS information. GPS data have limited spatial-temporal availability, particularly at a regional scale. Therefore, it lacks reliability to accurately estimate traffic conditions of a transportation network. This study proposes a novel methodology to address this problem. First, instead of estimating traffic conditions on a geographic road segment, traffic conditions are estimated for trip segments, which span multiple road segments. Second, machine learning methods are applied to classify traffic conditions. In this study, traffic conditions are defined as the combination of congestion level and road type. This study develops two machine learning models—a random forest (RF) model and a supervised clustering method—to classify traffic conditions, using trip characteristics such as average speed and acceleration. The two models are compared in relation to their accuracy and computational efficiency. Results show that speed-related trip characteristics, such as average instantaneous speed, are the most important variables for classifying traffic conditions in both methods. In addition, the proportion of idling in a trip is essential in distinguishing the Congested Highway and Uncongested Urban traffic conditions when applying the supervised clustering method. The comparison shows that the RF model has a higher estimation accuracy (81%) than the supervised clustering method (72%). Overall, this study shows that traffic conditions can be determined efficiently even in cases of limited GPS data.

Revealing the spatial variation in vehicle travel time with weather and driver travel frequency impacts: Findings from the Guangdong–Hong Kong–Macao Greater Bay Area, China

<abstract> <p>Vehicle travel time information is an essential location-based services that can be used to assess highway traffic conditions and provide valuable insights for transit agencies and travelers. To reveal the spatial variation in vehicle travel time with multiple factors, a multiple regression model and a geographically weighted regression model are used to investigate the associations between travel time and various factors. This study draws on freeway toll data in combination with local weather station records on Fridays over 12 months (286, 406 travel information data points), and the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China, is used as a case study for examining the influence of weather and driver travel frequency on vehicle travel time. The results show that i) travel frequency along an origin-destination (OD) route has a significant effect on travel time, and this effect is approximately 3 to 100 times that of other explanatory variables; ii) rainfall significantly impacts travel time, with an effect that is 1.9 to 8.26 times that of other weather factors; and iii) both weather and driver travel frequency factors display spatial heterogeneity. These findings provide valuable insights for both traffic management and freeway travelers.</p> </abstract>

Bluetooth 5 performance analysis for inter-vehicular communications

Previous work has demonstrated the feasibility of Bluetooth Low Energy (BLE) as an alternative technology for data transfers in inter-vehicular communication (IVC) scenarios. Bluetooth 5.x core specifications enhance the trade-off between energy requirements, communication range and flexibility. In this paper, we aim to analyse the potential of Bluetooth 5 features for VANET applications, proposing a connectionless communication system. By means of field experiments, we evaluate long range and 2 × speed features, defining a set of communication scenarios. This allows us to test both Bluetooth 5.x range and application throughput. The evaluation includes experiments of V2I communications carried out under real highway traffic conditions. The experiments conducted demonstrate that communication ranges up to 300 m may be achieved depending on the communications scenario. The results also show how throughput degrades as the distance between devices increases. The results obtained are used to discuss future work, aimed at deeper analysing Bluetooth 5 features for VANET applications, completing the development of our prototype and evaluating VANET connectionless communications with the features included in the latest Bluetooth 5.2 specification.

Impact of Accurate Detection of Freeway Traffic Conditions on the Dynamic Pricing: A Case Study of I-95 Express Lanes.

Express lanes (ELs) implementation is a proven strategy to deal with freeway traffic congestion. Dynamic toll pricing schemes effectively achieve reliable travel time on ELs. The primary inputs for the typical dynamic pricing algorithms are vehicular volumes and speeds derived from the data collected by sensors installed along the ELs. Thus, the operation of dynamic pricing critically depends on the accuracy of data collected by such traffic sensors. However, no previous research has been conducted to explicitly investigate the impact of sensor failures and erroneous sensors’ data on toll computations. This research fills this gap by examining the effects of sensor failure and faulty detection scenarios on ELs tolls calculated by a dynamic pricing algorithm. The paper’s methodology relies on applying the dynamic toll pricing algorithm implemented in the field and utilizing the fundamental speed-volume relationship to ‘simulate’ the sensors’ reported data. We implemented the methodology in a case study of ELs on Interstate-95 in Southeast Florida. The results have shown that the tolls increase when sensors erroneously report higher than actual traffic demand. Moreover, it has been found that the accuracy of individual sensors and the number of sensors utilized to estimate traffic conditions are critical for accurate toll calculations.

Highway Event Detection Algorithm Based on Improved Fast Peak Clustering

Aiming at the mining of traffic events based on large amounts of highway data, this paper proposes an improved fast peak clustering algorithm to process highway toll data. The highway toll data are first analyzed, and a data cleaning method based on the sum of similar coefficients is proposed to process the original data. Next, to avoid the shortcomings of the excessive subjectivity of the original algorithm, an improved fast peak clustering algorithm is proposed. Finally, the improved algorithm is applied to highway traffic condition analysis and abnormal event mining to obtain more accurate and intuitive clustering results. Compared with two classical algorithms, namely, the k-means and density-based spatial clustering of applications with noise (DBSCAN) algorithms, as well as the unimproved original fast peak clustering algorithm, the proposed algorithm is faster and more accurate and can reveal the complex relationships among massive data more efficiently. During the process of reforming the toll system, the algorithm can automatically and more efficiently analyze massive toll data and detect abnormal events, thereby providing a theoretical basis and data support for the operation monitoring and maintenance of highways.

IoT-Enabled Light Intensity-Controlled Seamless Highway Lighting System

Motivated by enormous highway-lighting energy consumption, smart lighting development is crucial to better manage available resources. While existing literature focused on ensuring cost-effective lighting, an equally important requirement, namely the visual comfort of motorists, is almost disregarded. This article proposes a novel Internet of Things-enabled system that can be intelligently controlled according to the traffic demand. Cooperative relay-network architecture is the central element that leverages upon placement of cyber-enabled lampposts to allow for sensing-exchanging highway traffic information. Data accumulation is exploited to automate adaptive switching on/off the lighting and provide backtracking detection of faulty lampposts. From the service provider's perspective, we envision to deploy low-cost highly durable sensing and network components to significantly cut down the operating cost. From the road user's perspective, the relay-network is envisaged to provide seamless driving experience where sufficient lighting is always perceived along the road. A critical analysis quantitatively evaluates the seamless driving experience considering car arrival rate, outage probability, and device malfunction probability. A road occupancy-based cost estimation analysis demonstrates the effective cost reduction of the proposal compared to existing systems. Furthermore, the performance of chosen communication modules under different setups is assessed through simulation, suggesting appropriate protocol for different highway traffic conditions.

Visualizing Potential Transportation Demand From ETC Log Analysis Using ELK Stack

Traffic conditions are among the issues most concerned with the general public, and the freeway is a large-scale Internet-of-Things application. In addition to obtaining real-time road usage information, analysis of local road usage habits is crucial in evaluations of government policy implementation. Using road usage data provided by the electronic toll collection (ETC) system, we investigated the data on road usage history on the freeways. The ELK stack was employed to construct a platform for visualizing real-time road usage information and history in this article; the platform is named the local transportation knowledge (LTK) platform. By analyzing more than 500 million pieces of data, the LTK platform proposed in this article efficiently visualized road usage data and facilitated the acquirement of local road usage knowledge. We verified that residents of other counties and cities commuted to Taichung each day. We also discovered that a considerable number of Taichung city residents were employed in Hsinchu Science Park and commuted between the two places. The LTK platform can present real-time freeway traffic conditions, facilitate in-depth analysis of local road usage data, and provide data to verify the relevant information.

Proactive highway traffic control with intelligent multi-objective optimisation algorithm

The great increase in car ownership has led to the daily recurrence of traffic congestion. Thus, traffic mobility, safety and emission concerns have become the most serious challenges for transportation researchers. To mitigate traffic congestion, a variety of proactive traffic-control strategies, such as ramp metering (RM), have been intensively investigated and deployed. With the aim of improving freeway traffic conditions, RM regulates the on-ramp flows dynamically in response to dynamic road conditions. However, most early RM strategies focus on optimising the traffic from one single aspect. This paper presents an RM control algorithm that predicts and evaluates the RM-controlled future traffic states. The impact of RM control was evaluated using a macroscopic traffic-flow model. The designed RM control algorithm possesses a multi-objective optimisation module, which improves the traffic network from the aspects of mobility, safety and emissions. The designed algorithm is evaluated through simulation and calibrated using field data collected over an 11 km major freeway stretch in Edmonton, Alberta, Canada. The comparison of the proposed algorithm-controlled scenario and the uncontrolled scenario shows that the proposed RM control algorithm can effectively relieve traffic congestion, improve safety and reduce carbon emissions concurrently.

The Prediction of Freeway Traffic Conditions for Logistics Systems

With a steady increase in the number of vehicles predicted, traffic congestion has become a significant logistical challenge. The increase in traffic not only results in pollution and traffic congestion, but also leads to increased travel time and productivity loss. Thus, traffic prediction has become an important research topic in the academia. In fact, logistics managers are more concerned about predicting short-term traffic conditions than the accuracy of prediction. Therefore, this study used a discrete-time Markov chain and online traffic monitoring data to predict the probability of traffic congestion and identify the freeway bottlenecks. The findings of the study revealed the high probability of National Freeway 3’s northern section being non-congested during the morning and afternoon rush hours. However, several bottlenecks were found in the links to nearby urban areas. The results of this study can not only facilitate logistics managers to optimize vehicle routes but can also support transportation control centers with regulating traffic flow in freeways during peak periods.

Exploration of Contributing Factors Related to Driver Errors on Highway Segments

A significant portion of crashes occurred on highway segments, with more than 90% of crashes associated with driving errors. To avoid a crash, a driver needs to detect a hazard, decide the safest driving maneuvers, and execute them properly. Driver errors at any of these sequential phases may lead to a crash; therefore, it is necessary to identify the contributing factors and assess their influence on driver behavior. To assist this investigation, a multinomial probit model was employed to study driver errors reported in crashes in rural and urban areas. The modeling results identified many highway geometric features, traffic conditions, roadway events, and driver characteristics as statistically correlated to different types of driver error. Following the extensive list, the impacts of error-contributing factors were discussed within each error category. This exercise helps to gain a better understanding of similar or varying effects of explanatory variables across different error categories. The broad and insightful information will help researchers and safety professionals to better understand when, where, and how the driver error may lead to a crash and to develop cost-effective preventive countermeasures.

Development of analytical procedure for selection of control measures to reduce congestions at various freeway bottlenecks

ABSTRACTVariable speed limit (VSL) and ramp metering (RM) affect freeway traffic operations in different ways and, accordingly, result in different effects on system travel time. The primary objective of this study is to propose an analytical procedure to help determine which control measure should be selected given different freeway bottlenecks and traffic conditions. The bottlenecks considered included an isolated merge bottleneck, a merge bottleneck with a closely spaced upstream off-ramp, and a diverge bottleneck with a closely spaced upstream on-ramp. Two RM and a VSL control strategies were considered, including the ALINEA, ALINEA/Q and feedback based VSL. The maximum achievable improvements in system efficiency by various control measures were calculated and the results were tested using modified cell transmission models. A coordinated control strategy that combined ALINEA/Q and VSL control was also proposed. The effects of VSL and RM control on system travel time at different freeway bottleneck areas were compared to identify the applicable conditions of different control measures. The analytical procedure was proposed for the selection of control measures at different bottlenecks and the ex-ante estimation of control effects were also discussed.

Impact of Heavy Vehicles on Highway Traffic Flows: Case Study in the Seoul Metropolitan Area

AbstractHighway capacity manuals (HCM) have incorporated the effect of heavy vehicles (HVs) in the form of passenger car equivalents (PCEs) for estimating highway traffic conditions, which include ...

Vision-based surveillance system for monitoring traffic conditions

With the rapid advancement of sensing technologies, it has been feasible to collect various types of traffic data such as traffic volume and travel times. Vision-based approach is one of the major scheme actively used for the automated traffic data collection, and continues to gain traction to a broader utilization. It collects video streams from cameras installed near roads, and processes the video streams frame by frame using image processing algorithms. The widely used algorithms include vehicle detection and vehicle tracking which recognize every vehicle in the camera view and track it in the consecutive frames. Vehicle counts and speed can be estimated from the detection and tracking results. Continuous efforts have been made for the performance improvement of the algorithms and for their effective applications. However, little research has been found on the application to the various view settings of highway CCTV cameras as well as the reliability of the speed estimation. This paper proposes a vision-based system that integrates vehicle detection, vehicle tracking, and field of view calibration algorithms to obtain vehicle counting data and to estimate individual vehicle speed. The proposed system is customized for the video streams collected from highway CCTVs which have various settings in terms of focus and view angles. The system detects and tracks every vehicle in the view unless it is occluded by other vehicles. It is also capable of handling occlusions that occurs frequently depending on the view angles. The system has been tested on the several different views including congested scenes. Vehicle counts and speed estimation results are compared to the manual counting and GPS data, respectively. The comparison signifies that the system has a high potential to extract reliable information about highway traffic conditions from highway CCTVs.

Safety analysis of passing maneuvers using extreme value theory

The increased availability of detailed trajectory data sets from naturalistic, observational, and simulation-based studies, is a key source for potential improvements in the development of detailed safety models that explicitly account for vehicle conflict interactions and various driving maneuvers. Despite the well-recognized research findings on both crash frequency estimation and traffic conflict analysis carried out over the last decades, only recently researchers have started to study and model the link between the two. This link is typically made by statistical association between aggregated conflicts and crashes, which still relies on crash data and ignores heterogeneity in the estimation procedure. More recently, an extreme value (EV) approach has been used to link the probability of a crash occurrence to the frequency of conflicts estimated from observed variability of crash proximity, using a probabilistic framework and without using crash records.In this study the Generalized Extreme Value distribution used in the block maxima (BM) approach and the Generalized Pareto Distribution used in the peak over threshold approach (POT), are tested and compared for the estimation of head-on collisions in passing maneuvers. The minimum time-to-collision with the opposite vehicle is used in both EV methods. Detailed trajectory data of the passing, passed and opposite vehicles from a fixed-based driving simulator experiment was used in this study. One hundred experienced drivers from different demographic strata participated in this experiment on a voluntary basis. Several two-lane rural highway layouts and traffic conditions were considered in the design of the driving simulator scenarios. Raw data was collected at a resolution of 0.1s and included the longitudinal and lateral positions, speeds and accelerations of all vehicles in the scenario. From this raw data, both methods were tested for stationary and non-stationary models. The latter allowed not-only for a better modeling performance in estimating the number of expected crashes, but also for a quantified analysis of the detailed driving choices affecting the head-on crash probability during passing maneuvers. The estimation results showed that the BM approach yielded more stable results compared to the POT approach, but the latter was able to produce crash rate estimates more consistently sensitive to the covariates of interest. Finally, the estimated distributions were validated using a second set of data extracted from an additional driving simulator experiment.The results indicate that this is a promising approach for safety evaluation. On-going work of the authors will attempt to generalize this method to other safety measures related to passing maneuvers, test it for the detailed analysis of the effect of demographic factors on passing maneuvers' crash probability and for its usefulness in a traffic simulation environment.

Observed and simulated traffic impacts from the 2013 Bay Area Rapid Transit strike

ABSTRACTDespite high costs, many cities build public transit to address regional equity, environmental and economic goals. Although public transit accounts for a minority of trips (∼5%), the impact is widely felt when service is suspended during a strike through excess road demand and slower journeys. In 2013, Bay Area Rapid Transit (BART) workers participated in two brief strikes, and the resulting traffic conditions illustrate the value of transit to drivers in the San Francisco Bay Area region. This paper tests the impact of rail transit service interruption on freeway traffic conditions using volumes and travel times. During the strike, regional freeway conditions showed negligible change. However, on facilities that parallel BART service, the impacts are as bad as the worst day of a typical week. Conditions on the San Francisco–Oakland Bay Bridge showed significant impacts with travel times and volumes nearly doubling the baseline median values on the worst day.

Freeway Travel Time Prediction Based on Seamless Spatio-temporal Data Fusion: Case Study of the Freeway in Taiwan

Travel time prediction has been the fundamental brick for developing both Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS). The problem of travel time prediction is primarily shaped by the data available for model development and the needs from the perspectives of both travelers and operators. A distance-based Electronic Toll Collection (ETC) system over the freeway system in Taiwan has been fully implemented since December 2013, which provides more comprehensive online data acquisition of freeway traffic conditions, thereby enabling the capability to more reliably predict of travel time over freeway segments. Based on the data from both the ETC system and traditional Vehicle Detectors (VDs), this research proposes a travel time prediction approach whose core technique of data fusion seeks to seamlessly capture the spatio-temporal pattern of freeway traffic flows by matching traffic dynamics revealed from the ETC and VD data. Further, prediction models are constructed thereupon, where the Kalman filter is employed for short-term prediction and the Fourier transform for long-term prediction based on the continuous parameterized modeling of spot travel speed. The proposed approach is implemented as an online deployable system using Java, together with real-world data collected from the freeway in Taiwan, for the numerical experiments. The prediction errors are no greater than 10% in most cases, which illustrates the high accuracy of prediction capability of the model. The encouraging results also highlight the benefits of the pre-processing of data and data fusion in improving data quality and applicability.

Comparative Study of a Super-Twisting Sliding Mode Observer and an Extended Kalman Filter for a Freeway Traffic System

Abstract Traffic state estimation represents one of the important ingredients for traffic prediction and forecasting. The work presented in this paper deals with the estimation of traffic state variables (density and speed), using the so called Super- Twisting Sliding Mode Observer (STSM). Several numerical simulations, using simulated and real data, show the relevance of the proposed approach. In addition, a comparative study with the Extended Kalman Filter (EKF) is carried-out. The comparison indices concern convergence and stability, dynamic performance and robustness. The design of the two observers is achieved using a nonlinear second order traffic flow model in the same highway traffic and geometric conditions.

Traffic Congestion and Air Pollution Exposure for Motorists: Comparing Exposure Duration and Intensity

This article investigates the effects of congested freeway traffic conditions on motorists’ exposure to traffic-related air pollution using real-world traffic data and a framework of established emissions and dispersion models. The intent is to isolate and compare the influences of congested traffic characteristics on exposure duration and exposure intensity. Mass inhalation of carbon monoxide (CO) and nitrogen oxides (NOx) is estimated for 45,226 simulated trips through a 14-mile congested freeway corridor. Results show that congestion increases total trip pollutant inhalation primarily through motorist delay (exposure duration), and to a lesser extent through increased concentrations (exposure intensity). The effects of varying wind and background concentration can be sufficient to obscure the influence of congestion on exposure intensity. The variability of exposure intensity due to traffic is mitigated by offsetting impacts among traffic flow, emissions rates, and pollutant dispersion. Exposure intensity increases with higher traffic flow and with lower traffic speed, but the impact of lower traffic speeds (through increased emissions rates and decreased dispersion) is smaller. The importance of exposure “hot spots” at traffic bottlenecks also increases in congestion. These findings suggest that traffic-based motorist exposure mitigation should focus on reducing travel duration on high-volume corridors through reduced vehicle flows (i.e., demand-side congestion mitigation). This analysis does not include non–congestion-based mitigation strategies such as cleaner vehicle engine technology or improvements in vehicle cabins, which can also reduce exposure intensity. On an individual scale, motorists can greatly reduce their own exposure during travel by, among other strategies, adjusting their departure time to less congested, lower volume periods.