Urban Arterial Road Research Articles

A novel modelling framework for real-time prediction of path travel times using both traffic and weather data

The problems of path travel time predictions have been studied over decades. Traditional approaches to this challenge have relied on different traffic data. However, in metropolitan cities with frequent rainfall, there is a need to consider the weather effects on real-time prediction of path travel times. Hence, this research integrates traffic data with weather data, including rainfall intensity data and weather forecast data to consider the temporal relationships between weather data and path travel times. The proposed modelling framework is evaluated with data from a major expressway and an urban arterial road in Hong Kong. Results unequivocally demonstrate that incorporating weather data significantly boosts prediction accuracy. This study also examines the impact of different frequencies on weather data, as well as weather forecast correctness, on prediction accuracy. Finally, the applicability of the proposed modelling framework has been verified without and with the input of ground truth on path travel times.

Refinement method of equivalent source power level determination for traffic noise in urban noise mapping

For traffic noise prediction, the predictive models of source power level determination commonly in use are the Technical Guidelines for Noise Impact Assessment in China or the Calculation of Road Traffic Noise model in the United Kingdom, which incorporate traffic flow and car speeds. However, the accuracy of these models is often compromised due to urban traffic's complexity, vehicle diversity, and unforeseen events. This study investigates a refinement method based on noise on-site measurement and aforementioned approaches for equivalent source power level determination for traffic noise in urban noise mapping. In the study, onsite measurements are conducted to collect noise and traffic data, and source power level calculated by refinement method is validated using measurement data. Different prediction methods are employed for measurement sites calculations. By comparisons between predictions with noise on-site measurement data, the refinement method proposed in the study demonstrates a significant accuracy enhancement in traffic noise prediction over previous models for determining equivalent source power level. This enhancement highlights the efficacy of adopting noise measurement-based methods for setting traffic noise source power levels, leading to more precise traffic noise predictions in complex urban settings, particularly in areas surrounding urban arterial roads.

Impact of Median Separation Facilities on Drivers’ Shy-Away Effects: Why Are Median Separation Facilities Widely Used in China?

In recent years, China has begun introducing readily installable median separation facilities into its urban road network. Although some studies have recognized the benefits of using median separation, few have investigated the effects of different types of facilities on driver behavior. This driving simulator study aims to determine whether different types of medians on urban arterial roads in China affect driving behavior, and to provide new insights for designing more effective and safer forms of median separation. A driving simulator study was conducted, with 50 participants. The scenario exactly reproduced an existing six-lane urban arterial road in China, and four different median separation types were tested: (1) double yellow line; (2) raised pavement markers; (3) reboundable traffic cylinders; (4) traffic separator railing. Their effects were statistically analyzed using data on driver eye movements, trajectories, and speeds collected from simulation tests. The results showed that the median separation facilities significantly improved drivers’ access to road information, corrected drivers’ attention, and suppressed driving speeds. Although median separation facilities can be an effective solution to the current problem of chaotic vehicular traffic on China’s urban roads, their setup still needs to be optimized. This study demonstrates the calculation method for optimal median facility setup. The resulting 1.21-m-high median facility, paired with a 1.17-m-wide median strip and a certain lane-width compression, more effectively addresses driver factors, the driving task, and the spatial right-of-way. The study’s calculations can also be used to improve other types of roads that can take a median separation facility.

Driving risk prediction of urban arterial and collector roads using multi-dimensional real-time data

With the objective of predicting driving risks on urban arterial and collector roads, this paper primarily collects and processes vehicle trajectory data and driver-vehicle-road-environment data. Using Entropy-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and K-means, risk scores and levels for the target roads are determined. An original dataset comprising 10,320 samples is established, with risk levels as labels and multi-dimensional data as features. SHapley Additive exPlanations (SHAP) analysis is utilized to extract important features. The performance of four ensemble models are compared, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Stacking. Three data resampling techniques are integrated to address data imbalance. The combination of LightGBM and Deep Convolutional Generative Adversarial Network (DCGAN) outperforms other approaches, showing potential for real-time risk prediction. Besides, DCGAN is employed to update the multi-dimensional features. The models' predictive performance is evaluated under various update ratios using F1-score and training time, with LightGBM demonstrating superior accuracy and efficiency compared to its counterparts (RF, XGBoost, and Stacking). This study develops a driving risk prediction system based on multi-dimensional features, offering a theoretical foundation and technical approach to enhance urban road traffic safety management.

A dynamic priority bus lane strategy in heterogeneous traffic environments

Allowing connected and automated vehicles (CAVs) to drive in bus lanes can enhance the utilization efficiency of exclusive bus lanes. However, this could also lead to a certain degree of heightened bus operating delays. In a connected and automated environment, vehicle-to-vehicle (V2V) communication is essential for implementing dynamic management strategies for bus lanes. This study focuses on urban arterial roads as the research scenario. Based on the characteristics of the car-following and lane-changing behaviors of human-driven vehicles (HDVs) and CAVs, a simulation platform for heterogeneous traffic flow is established. Four strategies for bus lane control are proposed: exclusive bus lanes (EBLs), bus and CAV mixed lanes (BCMLs), dynamic bus lanes (DBLs), and dynamic priority bus lanes (DPBLs). Through simulation experiments, an analysis is conducted to compare traffic operation characteristics under different bus lane strategies, assess the advantages of these strategies, and determine the traffic density conditions for their implementation. The results indicate that in comparison to the EBL strategy, the other strategies enhance traffic flows but result in different levels of travel delays for buses. Compared to the DBL and BCML strategies, the DPBL strategy is applicable under a broader range of traffic density conditions. Additionally, sensitivity analysis revealed that the effects of the V2V communication distance and bus flows on the applicability of DPBL strategies are relatively minor. These results provide a theoretical basis and practical guidance for future bus lane management.

Unveiling the impact of heterogeneous driving behaviors on traffic flow: A mesoscale multi-agent modeling approach

There are fewer simulation studies that comprehensively consider the impact of collision events due to heterogeneous driving behaviour on multi-lane traffic flow. This comprehensive study utilized multi-agent modeling to simulate the driver-vehicle-road system at the mesoscale level, integrating the characteristics of heterogeneous driving behavior. We developed a traffic flow simulation model for a three-lane urban arterial road segment and used rule-based algorithms to simulate vehicle following, lane changing, and collision behavior. A visualization simulation program was developed using NetLogo software to observe and analyze vehicle behavior. The findings revealed that collision events were the leading cause of traffic bottlenecks, impacting the continuity and coherence of traffic flow. Even after the collision event subsided and congestion eased, the average vehicle speed could not fully recover to its original level, reaching only about 70% of the maximum vehicle speed. Aggressive drivers exhibited distinct speed control strategies compared to conservative and ordinary drivers. This study demonstrates the effectiveness of multi-agent modeling in capturing the relationship between traffic bottlenecks and collision events, highlighting the influence of dynamic traffic events on driving behavior.

Driving risk identification of urban arterial and collector roads based on multi-scale data

Urban arterial and collector roads, while interconnected within the urban transportation network, serve distinct purposes, leading to different driving risk profiles. Investigating these differences using advanced methods is of paramount significance. This study aims to achieve this by primarily collecting and processing relevant vehicle trajectory data alongside driver-vehicle–road-environment data. A comprehensive risk assessment matrix is constructed to assess driving risks, incorporating multiple conflict and traffic flow indicators with statistically temporal stability. The Entropy weight-TOPSIS method and the K-means algorithm are employed to determine the risk scores and levels of the target arterial and collector roads. Using risk levels as the outcome variables and multi-scale features as the explanatory variables, random parameters models with heterogeneity in means and variances are developed to identify the determinants of driving risks at different levels. Likelihood ratio tests and comparisons of out-of-sample and within-sample prediction are conducted. Results reveal significant statistical differences in the risk profiles between arterial and collector roads. The marginal effects of significant parameters are then calculated separately for arterial and collector roads, indicating that several factors have different impacts on the probability of risk levels for arterial and collector roads, such as the number of movable elements in road landscape pictures, the standard deviation of the vehicle’s lateral acceleration, the average standard deviation of speed for all vehicles on the road segment, and the number of one-way lanes on the road segment. Some practical implications are provided based on the findings. Future research can be implemented by expanding the collected data to different regions and cities over longer periods.

Sustainable Development Methods of Urban Slow-traffic System Based on Human Factors Engineering

At present, China's urban transportation is in a period of development and transformation, and there are heated discussions about transportation sustainability. With China's new urban planning strategy and the future development trend of roads, the construction and development of the slow-traffic system have become a new direction of exploration. Through the analysis of human psychology and physiology, this article further explores the sustainable development of slow-traffic systems without sacrificing human welfare. Through the impact on the psychological safety of users based on distance in the street space, and through the three physiological stimulations of color and light on vision, noise on hearing, and ground materials on touch, it strengthens the sense of security for users in road space. Controllability and increased frequency of use. Integrate slow traffic into existing urban arterial roads and public transportation in more possible ways, reactivating street vitality and enriching street life. While achieving the "double carbon" goal, urban roads should be constructed meticulously to improve the quality of life of residents. Nowadays, there are many studies on the use of new technologies for road management, but few on road planning methods that focus on people themselves. After research and discussion, this article can fill the gaps in related research and contribute to the sustainable development of the slow-traffic system.

Sustainable Development Methods of Urban Slow-traffic System Based on Human Factors Engineering

At present, China's urban transportation is in a period of development and transformation, and there are heated discussions about transportation sustainability. With China's new urban planning strategy and the future development trend of roads, the construction and development of the slow-traffic system have become a new direction of exploration. Through the analysis of human psychology and physiology, this article further explores the sustainable development of slow-traffic systems without sacrificing human welfare. Through the impact on the psychological safety of users based on distance in the street space, and through the three physiological stimulations of color and light on vision, noise on hearing, and ground materials on touch, it strengthens the sense of security for users in road space. Controllability and increased frequency of use. Integrate slow traffic into existing urban arterial roads and public transportation in more possible ways, reactivating street vitality and enriching street life. While achieving the "double carbon" goal, urban roads should be constructed meticulously to improve the quality of life of residents. Nowadays, there are many studies on the use of new technologies for road management, but few on road planning methods that focus on people themselves. After research and discussion, this article can fill the gaps in related research and contribute to the sustainable development of the slow-traffic system.

Analysis of Factors Influencing Driver Yielding Behavior at Midblock Crosswalks on Urban Arterial Roads in Thailand

Globally, road traffic collisions cause over a million deaths annually, with pedestrians accounting for 23%. In developing countries, most pedestrian deaths occur on urban arterial roads, particularly at midblock crossings. This study analyzes the factors influencing driver yielding behavior at midblock crosswalks on urban arterial roads in Thailand. This study analyzed the factors influencing driver yielding behavior at the midblock crosswalk before and after the upgrade from a zebra crossing (C1) to a smart pedestrian crossing (C2), which is a smart traffic signal detecting and controlling pedestrians and vehicles entering the crosswalk. Video-based observations were used to assess driver yielding behavior, with multinomial logistic regression applied to develop driver yielding behavior models. The results revealed that the chances of a driver yielding at C2 were higher than at C1, and the yielding rate increased by 74%. The models indicate that the number and width of traffic lanes, width and length of crosswalks, vulnerable group, number of pedestrians, pedestrian crossing time, number of vehicles, vehicle speed, headway, post-encroachment time between a vehicle and pedestrian, and roadside parking are the significant factors influencing yielding behavior. These findings propose measures to set proper crosswalk improvements (e.g., curb extensions), speed reduction measures, enforcement (e.g., parking restrictions), public awareness campaigns, and education initiatives.

Study of Urban Arterial Road Off-Ramp Configuration in Highway Municipal Reconstruction

To reduce traffic congestion and improve the overall traffic capacity of roads, the municipal reconstruction of arterial roads from high-grade highways often involves setting up auxiliary roads on both sides of the road. This paper analyzes the existing methods of determining the configuration of urban arterial road off-ramps and proposes a novel design method, includes adding a “waiting for merging” section. Based on the traffic flow characteristics of the main roads, auxiliary roads, as well as off-ramps, second-stage deceleration theory and the insertable gap theory are applied. Considering the worst-case of diverted vehicles, a calculation model for the length of the urban arterial road off-ramps is proposed and utilized to calculate the advisory values for each segment of the off-ramps. Taking the reconstruction of an expressway in Hubei Province, China, as a case study, Vissim traffic simulation software was used to compare the proposed method with the traditional design method under four different traffic volumes. The results show that the proposed design method greatly improves the overall traffic capacity of roads compared with the traditional method, with a reduction of approximately 40% and 60% in delay time and queue length for the overall traffic, respectively. Furthermore, the research results can provide direct and effective design solutions and a basis for decision-making for setting the length of main and auxiliary road off-ramps.

Modeling of Traffic Noise along Urban Arterials in Irbid City of Jordan

Traffic-related noise pollution is a recurring problem in large and medium-sized cities. The objective of this study is to quantify traffic-noise levels along selected urban arterials and model the generated traffic noise based on traffic and pavement characteristics. Urban arterial and collector streets in Irbid city, Jordan, were taken as a case study. The city is considered an example of a medium-sized city. 65 urban arterial and collector sections were selected to achieve the stated objective. For each section, ten noise measurements were taken using a time interval of 2.5 minutes for each observation. The statistical pass-by method was used to measure 650 externalnoise observations. In addition, data on traffic, pavement and section geometric characteristics was obtained through field measurements. The collected traffic characteristics, including traffic flow, percentage of trucks and speed in each direction of travel, were obtained. In addition to the measurement of pavement macrotexture depth, an international roughness index was measured using a smartphone application. Investigation of the collected noise data indicated that urban streets experienced high noise levels, reaching maximum and average values of 82 and 77.2 dB(A), respectively. The results of the analyses showed that an increase in each of the included traffic characteristics resulted in significantly higher noise levels. For example, an increase in speed from 35 to 55 km/h would increase noise by 2.7 dB(A). In addition, the interaction term of roughness index and pavement macrotexture depth was found to increase the generated noise. Finally, the results of the analysis indicated that both multivariate linear -and exponential-regression models are suitable to model the generated traffic noise. Each model explained approximately 54% of the noise variability. Probably, traffic composition and vehicle-power type heterogeneity might reduce the level of explained variability. Keywords: Traffic noise, Urban arterials, Pass-by method, Noise modeling, Jordan.

Framework to Identify Vehicle Platoons under Heterogeneous Traffic Conditions on Urban Roads

Vehicle platoon studies are essential for understanding and managing traffic on urban arterial roads. The identification of vehicle platoons on urban roads has drawn more attention in recent years. Researchers have been exploring various methods and algorithms to detect and classify platoons, as well as investigating the benefits and implications of their presence on road capacity, safety, fuel consumption, and environmental pollution. The present study formulated a three-step strategy to identify vehicle platoons in the urban road network under heterogeneous traffic conditions. The proposed three steps are recognizing vehicle interaction, the estimation of critical headway, and vehicle platoon identification. Traffic data were collected for 13 h in a six-lane divided urban arterial road using an infrared sensor. A Python program was developed to recognize vehicle platoons. The results revealed that out of a total of 42,500 vehicles observed, 74% of vehicles were in vehicle platoons. The characteristics of the identified vehicle platoons were studied, thus focusing on key aspects such as platoon size, intra-platoon headway, platoon stream speed, and vehicle composition in the platoon. The results revealed a linear relationship between the percentage of vehicles in the platoon and traffic volume. The findings of the study will be beneficial in examining platoon-based data aggregation, the utilization of road capacity, and traffic flow optimization.

Digital Mapping of Urban Arterial Roads Pavement Conditions

Digital Mapping of Urban Arterial Roads Pavement Conditions

Driving Stress Partial Model Considering Time Domain Applying for Two Wheelers Drivers: A Case of Primary Arterial Roads

The physiological response of motorcyclists, which is substantially correlated with driving stress, can be utilised to assess traffic safety. Heart rate and respiratory rate can be used to measure driver tension using the heart rate variability method. This investigation was conducted in Aceh province along the urban arterial roads Jl. T. Nyak Arief and Jl. T. Muhammad Daud Bereueh. This study seeks to provide the anticipated empirical findings for determining the stress threshold of motorcyclists on Banda Aceh's primary arterial roads. The Polar Vantage V2 Smartwatch Multisport measures the pulse rate and respiratory rate of voluntarily participating motorcycle riders. This research was conducted using direct observation and direct observation related to volunteer travel activities as a sample, with volunteers selected at random and questioned about their willingness to participate. Direct interview surveys with volunteers were used to collect socioeconomic information about volunteers. The Multiple Indicators Multiple Causes (MIMIC) model empirically examined which road conditions and land use variables influence driving tension among motorists. The results revealed that the highest heart rate (HR) was recorded in the commercial land use area, at 94.029 beats per minute, while the highest HR was recorded at road bends, at 94.244 beats per minute. The time domain stress model is influenced by land use variables, with the most significant types of land use being offices and education, with the forming variables consisting of SDNN, RMSSD, and TINN.

Analysis of Street Vendor Effects on Urban Arterial Road

Analysis of Street Vendor Effects on Urban Arterial Road

Developing operating speed models for elevated multilane urban arterials using artificial neural networks

Operating speed models help assess and evaluate geometric design consistency along successive road segments. The development of operating speed models has been mostly focused on rural two-lane two-way highways, where horizontal curvature plays a dominant role in speed prediction. The need to enhance the prediction power of operating speed models and ability to capture more complex relationships within an urban setting have motivated this investigation. This research investigates the use of artificial neural networks, to develop operating speed models for multilane urban elevated arterial roads. Variables investigated in this study included geometric/operational features of a road segment in addition to the residual impact of the characteristics of upstream segments. A data collection exercise was undertaken on two major urban elevated arterial roads in Greater Cairo Region, Egypt: 6th of October and Saft Al-Laban corridors. Speed data was extracted from Google Distance Matrix Application Programming Interface and validated using test vehicle speed data. A regression-based modeling exercise was undertaken in the preliminary investigation phase to serve as a benchmark for the intended machine learning modeling exercise. Results showed that the prediction power of the developed ANN models — capturing the residual effect of upstream speeds — outperformed regression-based ones. The best-performing model used operating speeds of two upstream segments in addition to geometric/operational features of the segment under investigation to predict the segment operating speed (the model reported MAPE of 6.7%). Outputs of this model were used in a design consistency evaluation and potential transferability exercises to further investigate the model practicality.

Speed, Density, and Crash Relationship in Urban Arterial Roads

Vehicle speed has an essential role in the occurrence of traffic accidents. Increasing the number of vehicles operating on the road will further increase the risk of traffic accidents because it is not balanced with discipline in driving. This study's purpose is to analyze the relationship between speed and traffic accidents that occurred in Gadjah Mada and Hayam Wuruk Road, Jember, through several variations of calculations. The independent variables used are average speed and 85th percentile speed and density, while the dependent variable used is the accident rate. The analysis used is a correlation test that determines the level of closeness of the relationship between the two variables and a regression test that helps estimate the relationship between the two variables through a regression equation model that will be selected based on conditions in the field. The results of this study indicate that lower speed is associated with a lower accident rate. Meanwhile, lower vehicle density leads to an increase in the accident rate. Speed management to improve traffic safety in Indonesia is needed. Direction for future research is presented.

Factors Influencing Pavement Friction during Snowstorms

Operating an effective winter road maintenance program is a necessity for cities that face severe winter seasons. Snowstorms leave roads in a slippery surface condition that disrupts traffic flows and compromises drivers’ safety. Decision-makers use a variety of tools to control snow and ice on the roads, which include applying anti-icing chemicals before snowstorms, applying deicing substances on fresh snow, and clearing snow off the roads using snowplows. However, the influence of these tools on improving the overall road surface conditions has not been investigated. In this study, a location-specific and event-based framework was utilized to understand the impact of the different weather variables as well as maintenance operations on the variability of the pavement friction coefficients during snowstorms in urban environments. Using multilinear regression and ordinary least squares, friction coefficient models were calibrated. The final model was found to be a good fit for the data (R2 = 0.723). The model showed that the total precipitation during snowstorms, extremely low temperatures, and the potential for black ice formation worsen pavement friction significantly. On the other hand, plowing operations, the application of anti-icing chemicals before snowstorms, and frequent deicing operations all have a statistically significant impact on improving pavement friction. The model presented in this paper can be used to predict pavement friction on urban arterial and collector roads during snowstorms of different magnitudes, which could help the authorities in predicting the road surface conditions during forecasted snowstorms and deciding on the best course of action under these conditions.

Time optimization of urban arterial coordinated control based on multidimensional neighbourhood artificial bee colony algorithm

ABSTRACT Considering the traffic flow of the inner and outer intersection approach of urban arterial roads, an optimization control model of traffic arterial road signal parameters with signal cycle, green light time, and phase offset as the optimization variables is proposed. In order to solve the optimization model of timing parameters, the swarm evolution mechanism of artificial bee colony algorithm is improved and applied. The case study shows that the optimization model of timing parameters can collaboratively optimize the signal cycle, green light time and phase offset of the arterial coordinated control system, and the timing results of the optimization model by comprehensive consideration of inner and outer approach traffic flow are more beneficial to the improvement of traffic efficiency than the decoupling optimization algorithm. The proposed optimization model and solution algorithm can improve the effect of arterial coordinated control.