Urban Arterials Research Articles

Modeling the Traffic Flow of the Urban Signalized Intersection with a Straddling Work Zone

Work zones widely exist on urban roads in many countries and have a significant negative impact on traffic. Few studies have focused on modeling the traffic flow of the work zone on the urban arterials, especially on the work zone at the intersections. In this paper, a microscopic model based on the social force theory for the traffic flow of the intersection with a specific work zone, called straddling work zone, is proposed. The model can capture the no lane division and irregular boundary characteristics of the traffic of the intersection with a straddling work zone and also can reflect the interaction of the intersection traffic flows from the two opposite directions. The proposed model is calibrated and validated using the real work zone data, and the results display that the MARE values are all less than 10%. The factors affecting the traffic flow in the straddling work zone are analyzed through simulation. Our study reveals that the distance from the lower edge of the work zone to the median divider of the road and the proportion of large vehicles in the work zone have the greatest impact on the signalized intersection, which provides a reference for the future traffic control at the intersection with the straddling work zone.

Integration of Traffic Management and an Artificial Intelligence to Evaluate Urban Air Quality

Emissions from motor vehicles are the primary source of air pollution, especially in congested urban centres. However, through effective traffic management, it has been found that the level of pollution can be significantly reduced, facilitating the mobility of urban arterials. This study aims to quantify the extent of traffic emissions and to identify the influence of traffic management to improve air quality and reducing traffic emissions. An Adaptive Neuro-Fuzzy Inference System (ANFIS) model was developed to estimate the extent of traffic emissions (NO2 and PM10) at certain intersections. Then, a traffic management simulation software was also used to simulate traffic and to build a traffic improvement scenario at these intersections. This was followed by measuring the improvement in air quality due to traffic management modification, analysed using the developed ANFIS model. The results showed that reducing the delay at certain intersections may reduce NO2 and PM10 significantly. The proposed hybrid model increased the forecasting accuracy and improved the perception between the relationship between traffic characteristics and pollutant emissions. Additionally, it facilitates the work of city planners and helps decision making regarding urban air quality.

Analysis of accident duration on urban arterials roads

Acidentes de trânsito são o principal fator para a formação de congestionamentos não recorrentes em vias urbanas e responsáveis por gerar atrasos inesperados aos usuários do sistema. Entender o comportamento da duração desses acidentes e identificar os seus fatores explicativos pode conduzir para um gerenciamento mais eficiente do tráfego urbano. Buscando contribuir com essa problemática, este trabalho teve como objetivo analisar a duração de acidentes de trânsito ocorridos em vias urbanas da cidade de Fortaleza-CE. Inicialmente, um método para detectar a duração dos acidentes a partir de dados de tráfego de equipamentos de fiscalização eletrônica foi proposto, dada a indisponibilidade dessa informação no banco de dados de acidentes de trânsito. Como resultados, a duração dos 324 acidentes detectados teve uma média de 72 minutos e uma dispersão de 48 minutos. A função densidade de probabilidade Gamma apresentou o melhor ajuste à duração dos acidentes, indicando que essa variável é influenciada por eventos que ocorrem ao longo do tempo, aumentando a probabilidade de o acidente ser resolvido. A análise mostrou que fatores relacionados ao atendimento dos acidentes, ao comportamento dos indivíduos, ao número de veículos envolvidos e ao local de ocorrência (meio de quadra ou interseção) podem estar influenciando a duração dos acidentes. Entretanto, não foram encontrados indícios de que a duração dos acidentes é influenciada pela sua gravidade (acidentes com ou sem vítimas) ou pela sua natureza.

Vehicle Re-Identification With Image Processing and Car-Following Model Using Multiple Surveillance Cameras From Urban Arterials

In this paper, a vehicle Re-ID framework which integrates image processing and traffic flow model is developed. First, the CNN network is applied for vehicle detection and tracking and extracting attribute recognition. Particularly, attributes including vehicle color, type, make, and Re-ID feature are extracted to derive a similarity matrix between upstream and downstream vehicles. However, solely using these features could not achieve satisfaction matching accuracy. Our testing only shows a moderate accurate of around 72.3%. To further improve the Re-ID rate, this paper integrates visual information with the well-known IDM car-following mode. In our framework, IDM is first used to estimate the arrival time window for each upstream vehicle; and then with this time window derive a filter matrix which set the similarity as 0 for the matching vehicles outside the time window. Combining similarity matrix and filter matrix, the new developed Re-ID framework improves the matching rate to 95.7%. Furthermore, the proposed framework can even help identify vehicles that may have changed lanes, overtaken vehicles or driven on a sideroad. Such information is certainly valuable for future research on performance measure, traffic control, and congestion mitigation. Considering the significance of the trajectory data to nowadays traffic control and management and popularity of today’s surveillance cameras, this research certainly will contribute to the improvement of arterial traffic performance measure and efficient control.

Utilizing an Analytical Hierarchy Process with Stochastic Return On Investment to Justify Connected Vehicle-Based Deployment Decisions

With the increasing interest in connected vehicles (CV), it becomes all the more important to support decisions by transportation agencies to invest in Connected Vehicle to Infrastructure (V2I) applications. This paper presents a method that can be used to justify the investment in CV-based safety applications considering the availability of existing solutions. The method utilizes a combination of stochastic return on investment (ROI) analysis and a multi-criteria decision-analysis (MCDA) procedure to account for uncertainties, to consider effects that cannot be converted to dollar values, and to account for stakeholder priorities. The stochastic ROI analysis is applied using Monte Carlo simulations and included as part of the selection criteria in the MCDA method using the Analytical Hierarchy Process (AHP). This paper applies the method to support the deployment of CV-based applications to address transportation safety concerns on urban arterials. These applications can be categorized as CV-based support of signalized intersection safety, CV-based support of unsignalized intersection safety, and CV-based hazard warning applications. The results of the Monte Carlo simulation analysis for a project case study indicated the cost-effectiveness of these applications. The results of the AHP analysis indicate that utilizing V2I applications is 41.3% more favorable than utilizing the investigated existing solutions to address safety concern on the arterial facility that is the subject of the case study.

⋆This paper has been handled by associate editor Tony Sze.The application of novel connected vehicles emulated data on real-time crash potential prediction for arterials

Real-time crash potential prediction could provide valuable information for Active Traffic Management Systems. Fixed infrastructure-based vehicle detection devices were widely used in the previous studies to obtain different types of data for crash potential prediction. However, it was difficult to obtain data in large range through these devices due to the costs of installation and maintenance. This paper introduced a novel connected vehicle (CV) emulated data for real-time crash potential prediction. Different from the fixed devices’ data, CV emulated data have high flexibility and can be obtained continuously with relatively low cost. Crash and CV emulated data were collected from two urban arterials in Orlando, USA. Crash data were archived by the Signal for Analytics system (S4A), while the CV emulated data were obtained through the data collection API with a high frequency. Different data cleaning and preparation techniques were implemented, while various speed-related variables were generated from the CV emulated data. A Long Short-term Memory (LSTM) neural network was trained to predict the crash potential in the next 5−10 min. The results from the model illustrated the feasibility of using a novel CV emulated data to predict real-time crash potential. The average and 50th percentile speed were the two most important variables for the crash potential prediction. In addition, the proposed LSTM outperformed Bayesian logistics regression and XGBoost in terms of sensitivity, Area under Curve (AUC), and false alarm rate. With the rapid development of the connected vehicle systems, the results from this paper can be extended to other types of vehicles and data, which can significantly enhance traffic safety.

Results and lessons from local calibration process of the Highway Safety Manual for the state of Maryland: Freeway segments, speed-change lanes, and ramp terminals

In 2014, the authors successfully developed local calibration factors (LCFs) for the rural and urban arterials and highways for the State of Maryland. In this research, the authors computed LCFs for freeway segments, speed-change lanes, and signalized and stop-controlled ramp terminals to complement and complete LCFs for Maryland to adjust predicted motor vehicle crashes as part of the Highway Safety Manual (HSM) application. Implementing the HSM requires local calibration or development of jurisdiction-specific models. Either one is a laborious and challenging process because, despite being explained in the manual, the process is not easy or straightforward. Thus, the main contribution of this article is providing a detailed case study of the most complete calibration effort with the HSM Supplement models. Furthermore, the current state of the HSM Supplement adoption by different states is discussed. The results of the conducted research are summarized including the LCF development procedure and challenges that the authors had to overcome as well as some suggestions. LCFs were computed for data from a three-year study period (2008–2010) from multiple resources of Maryland government agencies. Ramps and collector-distributor (C-D) roads were excluded due to insufficient historical crash data. Baltimore City was also excluded because of a different data collection scheme.

GIS-based evaluation of the speed limit increase on urban arterial traffic safety in Ankara

Setting safe speed limit is very critical and complex phenomenon, which has to be decided based on the road hierarchy that categorizes roads according to their functions and capacities, and poses certain restrictions in the design process. However, this is challenging in urban regions of developing countries, where rapid city growth forces high-speed intercity roads to serve as urban arterials, along which traditional 50 km/h limit is not practically possible. To lower the speed variation (and provide mobility), speed limit increase is allowed as high as 82 km/h as in the case of urban arterials in Turkey. To understand the real impact of such controversial countermeasures, which is the main scope of this study, spatiotemporal distribution of accident hotspots along urban arterials are analyzed in GIS using nearest neighbor hierarchical (NNH) clustering; changes between 3 years before and after periods are evaluated using as hit rate (HR), predictive accuracy index (PAI), and recapture rate index (RRI) indices. For a case study of 7 urban arterials in Ankara, NNH analysis (with nmin = 5 and dmax = 250 m) resulted in 94 clusters in the before period, while there were 101 clusters in the after period; and decreased PAI values in the after case showed more accidents in the clusters, demonstrated no real improvement by speed limit increase. Application of safe system approach in the critical hotspots also showed that speed limit increase on the study corridors in Ankara was against the safe limits suggested by UNECE based on possibility of certain accident types.

Alternatives to Improve Operational Traffic in Roundabouts Using Microsimulation

Roundabout in urban arterials are likely to block roads or generate long queues of vehicles due to the constant changes on urban dynamics near to the interception. Several alternatives to modify the flow pattern or geometry and infrastructure need to be study previously to propose a final design solution of roundabout with traffic issues. In this study, a review of three alternatives to improve the operational traffic are discussed; signalized intersection, turbo-roundabouts and overpass/underpass. In addition, the application of these alternatives is evaluated in a “classic” roundabout located at Tunja city that has a maximum of 3691 Veh/h counted and it is operated in saturated condition. Microsimulation is used from a calibrated model with volume counted, vehicle composition and speeds in the VISSIM software. Results show that signalize the roundabout does not improve the operational traffic (with a reduction on 38% of approach speed) and, the turbo-roundabouts are a mid-term solution that meet the traffic demand on 15 years with a progressively loss of service (with a mean approach speed of 6.4 Km/h at 15 years). The constructions of underpass and overpass show to be the only solution that increase four times the travel speed throw the roundabout and keep acceptable speed at the end of cyclic live of intersection (18.7 Km/h at 20 years).

Modeling Traffic Flows on Urban Arterials Considering the Downstream Influence

Arterials are important transportation facilities, undertaking the two functions of mobility and accessibility. In the urban area, signalized intersections along arterials are usually closely spaced and bear heavy traffic pressure. Capacities of intersections can be reduced by downstream intersections even without having spillback. This effect will be accumulated and amplified back along the traffic direction and may lead to severe congestion in the upstream intersection which can be frequently observed, especially during peak hours. However, existing traffic simulators cannot capture this phenomenon accurately because they ignore the capacity drop before spillback happens. In this study, downstream influence is quantified by a virtual optimal speed ( vop). vop is the speed by which the upstream platoon reaches the endpoint of the queue ahead when the last vehicle in the downstream queue just starts. Based on that, two piecewise regression models, for start-up lost time and saturation flow rate, are formulated to estimate the capacity reduction. These regression models are further introduced to improve the modified cell transmission model (CTM). The result of the simulation experiment shows that the proposed CTM model has better performance in simulating traffic flow on signalized arterials than the existing CTM, especially in reproducing the traffic congestion in upstream. The analysis emphasized the importance of considering the downstream influence when simulating the traffic on signalized arterials. Finally, a sensitivity analysis is designed to further reveal the causes of upstream congestion.

Examining the Characteristics of Heterogeneous Traffic at Various Lane Closures

The primary aim of this study is to support the policy decisions on selection of a work zone layout that would have least deterrence to the traffic flow on busy urban arterials carrying heterogeneous traffic. The traffic flow characteristics were recorded with two cameras and the speed variation was obtained by plying probe vehicles fitted with Global Positioning Systems (GPSs) repeatedly in the section. The traffic stream speed and capacity of the various types of work zone sections were analyzed and it is found that near side lane closure has a relatively least effect on capacity (15 %) whereas the effect of run around type closure is maximum (46 %). The reasons for the variations in speed and capacity at these types of work zones were examined and reported. Besides, the application of the study results with respect to planning of work zone layout is discussed.

Review Paper on Vehicle to Vehicle Communication for Crash Avoidance System

The objective of this paper is to provide a brief description on the proposed technologies to predict the collision between vehicles before the collision. The spectacular increase in traffic increases the demand for innovative technologies that can improve the safety and efficiency of transport systems. Road safety can be significantly improved by the use of wireless communication technologies for vehicle networks, which allows new services such as collision detection traffic management and other communication devices between moving vehicles. This paper presents an implementation of the complete vehicle-vehicle communication based on Li-Fi technology and DSRC technology based on Wi-Fi. The proposed system solves many of the problems faced by existing systems by using a GPS module instead of the conventional speedometer and also uses sensors that are reliable in areas where human intervention is involuntary or life-threatening in danger of extinction. The problems of congestion in the urban arteries increase day by day, and it is very difficult to deal with them in emergencies. Therefore, within the system, a communication unit is developed that interacts with other vehicles to free the lanes. This system aims to communicate with the vehicle in its environment using its location (i.e., using latitude and longitude) to indicate proximity. If these vehicles are in the immediate vicinity, drivers are notified by a message. In this way, drivers can communicate with each other and act according to the situation.

How does an increased freeway speed limit influence the frequency of crashes on adjacent roads?

Urban arterial roads carry the most traffic on urban road networks and experience the highest percentage of crashes in urban areas. Safety on urban arterials that are adjacent to a higher speed freeway may be impacted by speed spillover or adaptation. The objective of this study was to determine the effects of raising freeway speed limits on the frequency of crashes on urban arterial roads adjacent to freeways (spillover effects). Crash data within Michigan were collected on 1393 urban arterial road segments before and after freeway speed limits were altered. Before-and-after data was collected simultaneously on 1470 comparison segments of urban arterial where speed limits did not change to control for the regression-to-the-mean bias. The mixed effects negative binomial (MENB) regression model was developed to analyze crash frequency on urban arterials. The results indicate that raising speed limits of freeways by as little as five miles per hour had a likelihood of increasing crash frequency on adjacent arterial roads by as much as 13.9 percent. To investigate if the safety impact of speed spillover changes with the distance from the freeway, influence areas (0–1 mile, 1–2 mile, and 2–3 mile) were used. The findings of this study provide insights into the effects of speed spillover on crash occurrences, and it demonstrates that increasing freeway speed limit has a negative influence on driver compliance with the speed limit on adjacent arterial roads. Correspondingly, the influence of freeway speed on drivers’ speeding behavior on adjacent urban arterials fades away with the distance from the freeway.

Developing Capacity Reduction Factors for Curbside Bus Stops Under Heterogeneous Traffic Conditions

Traffic characteristics of a roadway are significantly influenced by bus stops, and it becomes more chaotic when a separate bus bay is not provided and the bus has to be stopped in the curb lane. The present study analyzes the effect of such bus stops on the midblock capacity of urban arterials under heterogeneous traffic conditions. Field data have been collected at six sections of six-lane divided urban arterial roads; out of that, three sections have not marked with bus stop and referred to as base sections, whereas the other three have designated bus stops and denoted as friction sections. A set of simultaneous equations are established using speed and flow data to estimate the midblock capacity of urban roads and found to be 6000 PCU/h for one direction of flow. Further, a set of simultaneous equations are developed between the speed of a vehicle type and the densities of all categories of vehicles with densities of stopped buses in their individual terms. These equations are solved for any number of stopping buses at a fixed proportion of vehicles in the traffic stream for different traffic volume levels and thus get the capacity curves. It has been found that the capacity of urban arterials is reduced by 6.6–28.3% when the frequency of stopping buses is as low as 20 bus/h to as high as 120 bus/h, respectively. The study outcomes are useful for designing bus stops and at the same time estimating the capacity of such sections in order to reduce the congestion on the urban roadway network.

Safety Evaluation of Arterials under Signal Coordination Considering the Correlated Heterogeneity and Multivariate Spatial Correlation

AbstractSignal-coordinated control systems have been widely implemented on urban arterials. By synchronizing consecutive intersections, signal coordination can significantly improve the throughput ...

An Independent Trajectory Advisory System in a Mixed-Traffic Condition: A Reinforcement Learning-Based Approach

Abstract Achieving smooth urban traffic flow requires reduction of sharp acceleration/deceleration and accordingly unnecessary stop-and-go driving behavior on urban arterials. Traffic signals at intersections, and induced queues, introduce stops along with increasing travel times, stress and emission. In this paper, an independent reinforcement learning-based approach is developed to propose smooth traffic flow for connected vehicles enabling them to skip a full stop at queues and red lights at urban intersections. Two reward functions, i.e., a fuzzy reward engine and an emission-based reward system, are proposed for the developed Q-learning scheme. Another contribution of this work is that the necessary information for the learning algorithm is estimated based on the vehicle trajectories, and hence, the system is independent. The proposed approach is tested in a mixed-traffic condition, i.e., with connected and ordinary vehicles, via a realistic traffic simulation with promising results in terms of flow efficiency and emission reduction.

Use of eye tracking device to evaluate the driver’s behaviour and the infrastructures quality in relation to road safety

Eye tracking allows to obtain important elements regarding the drivers’ behaviour during their driving activity, by employing a device that monitors the movements of the eye and therefore of the user’s observation point. In this paper it will be explained how analysing the behaviour of the drivers through the eye movements permits to evaluate the infrastructures quality in terms of road safety.Driver behaviour analysis have been conducted in urban areas, examining the observation target (cars, pedestrians, road signs, distraction elements) in quantitative terms (time of fixing each singular target). In particular, roundabout intersections and rectilinear segment of urban arterials have been examined and the records related to seven drivers’ behaviour were collected, in order to have a significant statistical variability. Only young people has considered in this study.The analyses carried out have made it possible to assess how different types of infrastructure influence the behaviour of road users, in terms of safety performance given by their design.In particular, quantitative analyzes were carried out on driving times dedicated to observing attention rather than distraction targets. From a statistical point of view, the relationship that exists between the characteristics of the driver, weather conditions and infrastructure, with driving behavior (traveling speed and attention / inattention time) was analyzed by ANOVA method.

Do street status and centrality matter for post-socialist memory policy? The experience of Ukrainian cities

Naming and renaming of urban space often is sensitive in terms of the street location and status and implies categorization of streets according to the perceived importance of a street name. Thus, different locations in the city have different symbolic significance, and the urban toponymy could be read as a spatial projection of the societal axiological system. This article represents an attempt to study the importance of location (centrality vs. peripherality) and status (significance) of the urban public spaces in the 36 largest Ukrainian cities in terms of symbolical value and memory policy. The findings indicate that both investigated factors constitute an important tool of identity shaping and historical memory policy, but their influence and manifestation may vary considerably depending on specific historical, cultural and (geo)political conditions. Therefore, although the central parts of cities and the main urban arteries have tangibly larger symbolic significance, the toponymy of less presentable urban areas may be no less eloquent in the critical toponymy studies.

Real-time crash risk prediction on arterials based on LSTM-CNN

Real-time crash risk prediction is expected to play a crucial role in preventing traffic accidents. However, most existing studies only focus on freeways rather than urban arterials. This paper proposes a real-time crash risk prediction model on arterials using a long short-term memory convolutional neural network (LSTM-CNN). This model can explicitly learn from the various features, such as traffic flow characteristics, signal timing, and weather conditions. Specifically, LSTM captures the long-term dependency while CNN extracts the time-invariant features. The synthetic minority over-sampling technique (SMOTE) is used for resampling the training dataset. Five common models are developed to compare the results with the proposed model, such as the XGBoost, Bayesian Logistics Regression, LSTM, etc. Experiments suggest that the proposed model outperforms others in terms of Area Under the Curve (AUC) value, sensitivity, and false alarm rate. The findings of this paper indicate the promising performance of using LSTM-CNN to predict real-time crash risk on arterials.

Short-term traffic flow prediction based on faded memory Kalman Filter fusing data from connected vehicles and Bluetooth sensors

This paper proposes a Kalman Filter (KF) technique to predict the short-term flow at urban arterials based on the information of connected and Bluetooth equipped vehicles. Online traffic flow prediction using real-time data derived from different sensors is still an open research subject. To this end, a Kalman Filter model is developed to predict traffic flow based on two sources of real-time data, i.e. Connected Vehicles (CVs) and Bluetooth data. We also apply a Faded Memory Kalman Filter (FMKF) by considering more weights for new measurements to overcome the issue of inaccuracy in the prediction model and to predict the traffic flow with more resolution. At first, based on training data from Vissim traffic simulator, parameters of the KF's equations are calibrated using a machine learning based and big data processing. Performance of the conventional and faded memory KF models are then validated and compared using some test data pertaining to different rates of connected vehicles and Bluetooth-equipped vehicles (BVs). We use a pilot study of the city of Melbourne, Australia for numerical tests. The results indicate significant superiority of the FMKF over the KF in various traffic situations, as such the prediction error in some cases has reduced up to 60%. This paper contributes to the literature in three folds: (i) It uses a computationally efficient flow prediction algorithm based on synthesizing data from CVs and BVs (ii) It proposes to use an adaptive form of KF (i.e. FMKF) to compensate for the prediction error originating from modelling error. Hence, the model can perform well for a range of traffic conditions (iii) The proposed model works well even with low penetration rates (PRs) of the CVs or BVs (say 20%).