Heterogeneous Traffic Conditions Research Articles

Short-term traffic flow prediction in heterogeneous traffic conditions using Gaussian process regression

Short-term traffic flow prediction in heterogeneous traffic conditions using Gaussian process regression

Influence of pedestrian movement on speed of vehicles, capacity and level of service of urban midblock sections

Side friction factors are all those actions related to the activities taking place by the sides of the road and sometimes within the road, which interfere with the traffic flow on the travelled way. They include but not limited to pedestrians, bicycles, non-motorized vehicles, parked and stopping vehicles. These factors are normally very frequent in densely populated areas in developing countries, while they are random and sparse in developed countries making it of less interest for research and consequently there is comparatively little literature about them. The objective of this thesis is to analyze the effect of these factors on capacity of urban roads. The present study is mainly focused on individual effect of the friction factors like pedestrians crossing the road on capacity of urban roads. Data on two sections of 4-lane divided road were collected in Hyderabad with pedestrian cross flow. These are taken as the base sections. Three basic parameters of traffic speed, volume and density are used for estimation of traffic carrying capacity of a road. For determination of speed-volume relationship in heterogeneous traffic condition, the volume calculated by total vehicles recorded for each counting period were converted into equivalent number of PCUs. Mean stream speed or weighted space mean speed is calculated and used in the present study. The present study demonstrates that mix traffic stream can be converted in to an homogeneous equivalent of passenger cars by multiplying the total traffic volume (in veh/hr) by a stream equivalency factor (K). It will avoid the problem of estimation of PCU values for individual vehicle categories in the traffic stream. The stream equivalency factor (K) will depend on traffic composition and a regression equation is developed in this study to estimate K-value on urban roads.

Analysis of roadway capacity for heterogeneous traffic flows considering the degree of trust of drivers of HVs in CAVs

Future traffic flows will be driven by a mixture of CAVs and HVs. Under heterogeneous traffic flow conditions, drivers of HVs will change their following distance when driving behind CAVs due to psychological factors, which will affect road capacity. This study builds a heterogeneous traffic flow model based on the Markov chain theory, which can derive the proportions of different car-following types under different vehicle distributions. The roadway capacity model is constructed by comprehensively considering the penetration rate of CAVs, platoon intensity, maximum platoon size, and the driver's trust degree of CAVs, and the effects of the four factors on the roadway capacity are analyzed through numerical experiments. Finally, the theoretical model was validated using python-sumo joint simulation experiments. The results show that increasing the degree of trust that HV drivers have in CAVs can further enhance roadway capacity, and the effect of the degree of trust on capacity becomes progressively smaller as the platoon intensity increases. It was also found that platoon intensity can be harmful to the improvement of capacity when the degree of trust is high and the maximum platoon size is small.

Development of Capacity Estimation Models for Multi-lane Roads under Heterogeneous Traffic Conditions

The objective of this study is to develop capacity models to estimate the traffic carrying capacity of multi-lane roads with heterogeneous traffic flows. Since capacity is an important parameter in traffic engineering studies, it is vital that it is accurately estimated. Most of the current capacity estimation methodologies available are developed based on homogeneous traffic flows and hence not conducive for use in heterogeneous traffic conditions observed in Sri Lanka. Therefore, this study is done to fulfill the requirement of a capacity estimation model for this purpose. Fifty midblock sections were surveyed using manual flow data collection methods and a novel Google Application Program Interface (API) based method for speed data collection. Based on the collected data, capacity values were developed following the fundamentals of traffic flow. Regression models were built to estimate four-lane and six-lane capacity values based on roadway characteristics such as effective lane width, access point density, median type, and built environment condition. Separate models were developed because the impact these characteristics have on four-lane and six-lane roads are different. The models showed good fit with R2 values of 0.81 and 0.86 for four-lane and six-lane roads, respectively. The base capacity value of a four-lane urban road was estimated to be 2044 pcu/h/l and for a six-lane sub-urban road section 2108 pcu/h/l. The outcomes of this study can be used to develop capacity guidelines in countries with heterogeneous traffic conditions for capacity estimation.

Genetic Programming for Estimating Passenger Car Equivalent in Unsignalized Intersections

The study concentrates on two different Genetic Programming approaches for determining Passenger Car Equivalent(PCE) values and observing the impact on capacity estimation at urban unsignalized intersections. Considering heterogeneoustraffic conditions, a new PCE value is introduced to encompass sustainable modes of public transit vehicles, specifically Slow-moving three-wheelers (SM3W), commonly known as E-Rickshaws. Since PCE value is considered an important parameter forcapacity calculations, the present study considered 14 unsignalized intersections located in Ranchi city of India. An automaticplate recognition system is employed to have the count of vehicular traffic. The methodologies include Age layered populationstructure genetic programming (ALPSGP), and the Offspring selection genetic programming (OSGP) approach that incorporatesstatic and dynamic variables. Based on the significance test and ranking of the Genetic programming (GP) models, the OSGPmodel is recommended as the most appropriate model for heterogeneous traffic. Sensitivity analysis reported that laggingheadway (����) is the most contributing factor in PCE estimation. The PCE value of SM3W is found to be 0.81 and that could beincorporated as a new classification of vehicles in Indo-HCM. It is observed that evaluated capacity based on OSGP’s PCEvalues performed admirably in both normal and congested traffic situations.

Simulation of Traffic Flow under Heterogeneous Traffic Conditions using VISSIM

Simulation of Traffic Flow under Heterogeneous Traffic Conditions using VISSIM

Estimation of vehicle control delay using artificial intelligence techniques for heterogeneous traffic conditions

The conventional standardized theoretical models (such as Webster, Alcelik, Indo-HCM) used for the delay estimation revolve around the mathematical hypothesis and assumptions, making them static with limitations in accommodating the dynamic traffic behaviors. Recent advances in artificial intelligence make machine learning techniques suitable for estimating vehicle control delay compared to conventional methods. This paper demonstrates the application of several machine learning models developed by focusing on the fluctuations of traffic observed at an intersection having heterogeneous traffic conditions in Ahmedabad city for delay estimation. Several parameters are extracted from on-field and video surveys. However, not all parameters are relevant for accurately estimating vehicle control delay. Hence, a feature selection process consisting of several feature-scoring techniques from filter, wrapper, and embedded methods is applied to all the parameters. This process gave insights into the most statistically relevant independent parameters, and out of all the parameters, cycle time was found to be insignificant, with a feature score of 0 from almost all techniques. Hence, it was removed,and then the prominent parameters were then used to build a vehicle control delay model using support vector regression (SVR), K-nearest neighbor (KNN), artificial neural network (ANN), random forest regression (RF), and decision tree regression (DT). Error distribution, standard deviation of errors, coefficient of Determination (R2), and Root Mean Squared Error (RMSE) are the parameters used for evaluating the performance of the machine learning models. RF outperformed them all with a standard deviation of errors, R2, and RMSE of 11.065, 0.926, and 11.081 on testing data. But ANN, KNN, and DT also performed satisfactorily. Compared with conventional standardized theoretical models, all the machine learning models except SVR performed better.

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.

Motorized Two-Wheeler Overtaking: An Analysis of Strategies and Factors Affecting Overtaking Duration on Undivided Highways in Heterogeneous Traffic Conditions

The increasing penetration of motorized two-wheelers (MTWs) on road networks of developing countries and their growing share in road crashes necessitate efficient driver assistance systems to support drivers in risky and hazardous situations. MTWs exhibit complex maneuvers while driving. Overtaking is one such complex maneuver from a safety viewpoint. While a few studies have explored this topic, none have modeled overtaking duration in heterogeneous traffic conditions. The present study investigates the overtaking behavior of MTWs and identifies the factors affecting the total overtaking duration using a hazard-based duration approach. For this purpose, a field study was conducted using instrumented MTWs, and a total of 250 overtaking maneuvers were observed in which 154 (62%) occurred in the same lane, called overtaking with lane sharing. The remaining 96 (38%) happened with a lane change, referred to as overtaking with lane changing, which is a unique finding with respect to overtaking strategies opted by MTW riders. It was found that the mean overtaking duration and overtaking distance for lane sharing were 5.2 s and 63 m, and for lane changing were 5.4 s and 59 m, respectively. Subsequently, hazard-based duration model results reveal that overtaking duration was found to be positively related to the initial gap, final gap, speed of the overtaken vehicle, and multiple overtaking. In contrast, the speed of the overtaking (instrumented MTW) vehicle has a negative impact on overtaking duration. The study findings will enable the development of an advanced rider assistance system for overtaking support of MTW riders.

Surrogate safety evaluation of curve sections on an interurban highway under heterogeneous traffic conditions in India

<p class="H1">Road traffic safety is a global concern. Conventionally, road safety evaluation is carried out by analyzing historical crash data, which is a reactive approach to safety analysis. The safety analyst has to wait for sufficient time to accumulate crash data before taking up any safety analysis as a sample of substantial crashes is required for analysis. But in safety analysis with a proactive approach no crash data is required, which is replaced with surrogate “conflicts”, which can be obtained from the new techniques of traffic conflict technique and surrogate safety parameters. Other approaches can be applied in traffic safety evaluation in anticipation of the crash occurrence. The advent of traffic conflict techniques, i.e., use of traffic conflicts in place of crashes and microsimulation methods like modeling of the traffic flow and pattern in traffic streams on a road network, started to apply as a method of analyzing microscopic simulation models and traffic conflict techniques to determine the safety issues in traffic systems and correlate them to the probable incidences of collisions. In this regard, surrogate safety parameters have been used to determine the level of safety on the typical curve sections of an interurban highway namely, Faridabad-Gurgaon four-lane divided highway in Haryana, India. This is accomplished by the use of vehicle trajectory data extracted through microscopic simulation in Verkher in Staedten Simulation (VISSIM) and analysis in Surrogate Safety Assessment Model (SSAM) for the above-referred corridor. Further, efforts have been made to present the intensity of traffic conflicts happening at the curved sections. The surrogate safety measures time to collision (TTC), deceleration rate (DR), and change in velocity (<em>ΔV</em>), as well as conflicting vehicle speed (Max <em>S</em>), are obtained by analysis from the SSAM model for all the curve sections and are validated using the reported crash data on the curve sections of the candidate corridor for 3 years. With the help of statistical elaboration, the critical threshold for TTC with the heterogeneous traffic movement is found 1.6 s, meaning any conflict occurring less than this time would invariably lead to a fatal crash. Similarly, the critical deceleration rate is observed as 0.569 m/s<sup>2</sup>, implying that any conflict with more than this value may lead to a fatal crash. Further, the Delta<em>V</em> values deduced for the study corridor on interurban curve sections catering to heterogeneous traffic movement is 4.1 m/s. Again, any conflict more than this value can turn into a potential crash.</p>

User Opinion on Factors Affecting Drivers Frequent Lane-Changing Behavior: A National Sample of Drivers in Pakistan

Abstract: In recent years massive traffic volume increase has demanded the implementation of traffic improvement plans in Pakistan. The urban Highway transportation networks are becoming congested in developed as well as in most of the developing countries. The main causes of unmanageable heterogeneous traffic congestion on the highway are an increase in population, change in lifestyle, and economic development of a country. To recognize the characteristics of a driver who frequently changes lanes and analyze the techniques of frequent lane-changing behavior. The researchers collected data via a questionnaire survey and analyzed the influence of traffic environment and driver characteristics violation on frequent lane-changing behavior on travel speed in detail by manually Excel statistics. Results showed that the impact of a heterogeneous traffic environment of two lanes and three lanes cars, and traffic flow density was sharply associated with frequent lane-changing behavior. The drivers were the main factors affecting frequent lane-changing behavior in heterogeneous traffic conditions, which was most popular during aggressive driving. Only 8.33% of drivers were never involved in this behavior. Most liable to frequent lane-changing behavior were males as compared to females, 31–40-year-old drivers, and those with low academic qualifications and private vehicle personalities. Among various traffic factors, changes in required speed had the most significant impact on how often lane changes occurred. The findings of the current study highlight the most significant traffic parameters that influence the lane change frequency. This research recommends strict enforcement of lane discipline to counter traffic congestion and the remedial measures that are to be taken for the improvement of better and sustainable road infrastructure conditions. Overall the research study will have practical implications for improving traffic management and safety on highways, and it provides a foundation for further research in the field of traffic-related technologies.

The Evaluation of the Impact of Road Width on Passenger Car Units (PCU) in Heterogeneous Traffic Conditions

This study delves into the utilization of Passenger Car Units (PCU) as a pivotal metric for gauging the influence of diverse vehicle types on traffic flow. Focusing on the context of Mogadishu, data acquisition was conducted across eight key sections of major highways. Employing digital video recorders, the gathered data facilitated a comprehensive analysis of traffic dynamics, thereby enabling the computation of PCU values. The investigation unveiled a discernible trend wherein the traffic makeup, comprising bus, truck, light commercial vehicle (LCV), and car categories exhibited a positive correlation with carriageway width expansion. Nevertheless, the volume composition peaked notably in narrower carriageway widths. Concurrently, the speed of the aforementioned vehicle categories showcased a linear progression proportional to carriageway width augmentation. Intriguingly, PCU values assigned to motorcycles consistently undershot the figures stipulated in the Mogadishu Road Standards (MRS). Conversely, PCU values for bus, truck, and LCV categories consistently surpassed the MRS 2070 benchmarks. This scrutiny distinctly elucidated the intricate interplay between lane width and PCU values across distinct vehicle types, effectively underscoring the propensity of PCU values to escalate with escalating carriageway dimensions.

Proposing an effective approach for traffic safety assessment on heterogeneous traffic conditions using surrogate safety measures and speed of the involved vehicles

Objective The vehicular traffic in the cities is becoming more complex in developing countries like India due to the rising population and rapid urbanization. With the annually increasing road accidents, a study on the effective safety assessment of heterogeneous traffic conditions is needed. The study aims to evaluate the safety of heterogeneous traffic by spotting the critical conflicts with respect to the speed of the involved vehicles. Methods The current study proposed Critical Following Speed instead of using a single threshold value for safety assessment in mixed traffic. Critical Following Speed was proposed by comparing the stopping and the available distances between the involved vehicles and used to identify the critical conflicts. With this, the study uses the speeds of both the leading and the following vehicles to judge the nature of the conflict. Three unsignalized four-legged intersections (S-1, S-2, and S-3) and two straight road sections (S-4 and S-5) were selected as the study area in Trichy, India. Post Encroachment Time (PET) and Time to Collison (TTC) were used as surrogate indicators to assess the crossing or merging and rear-end conflicts, respectively. Results The average PET and TTC values were between 1.25 and 1.73 s in the study locations. The overall percentage of critical conflicts indicated the non-safer crossing and merging maneuvers in three locations. The other two locations were experiencing safe rear-end conflicts, as the percentages of critical conflicts were below 4%. Various combinations of leading and following vehicle types were examined for the contribution of critical conflicts. The proposed methodology was validated with the 4-year accident data and a good relation was obtained. Conclusions Fast-moving vehicles were responsible for the less safe maneuvers with a higher collision probability in all the study locations. The correlation between critical conflicts and road accidents shows the effectiveness of the proposed approach in the traffic safety assessments for mixed traffic. This approach could be employed even in countries with homogenous traffic conditions instead of using a single threshold value. The correlations also show the potential of the proposed Critical Following Speed as a surrogate safety indicator for safety evaluation in the future.

Real-world tailpipe emissions from autorickshaws (3-wheelers) under heterogeneous traffic conditions.

The current study aimed to measure real-world emissions of three-wheeled autorickshaws powered by CNG and parameters (such as speed, acceleration, air-fuel (A/F) ratio, and rpm) influencing 3-wheeler emission rates. Test vehicles manufactured under Bharat Standards BS-III and BS-IV were monitored for exhaust emissions in Delhi city using a portable exhaust emission measurement system (AVL Ditest Gas 1000). The average emission rates of CO, HC, and NO gases for on-road autorickshaws were found to be 0.015 ± 0.017, 0.003 ± 0.0017, and 0.007 ± 0.005 g/s, respectively. Further, the highest emission factor values of 3.98 g/km and 3.93 g/km were estimated for CO and HC+NO gases, respectively. These values were found to be 1.4-3.2 times higher than the respective BS emission norms (BS III-CO =1.25 g/km, HC+NO = 1.25 g/km; BS-IV-CO = 0.94 g/km and HC+NO = 0.94 g/km). In this study, it was observed that the driving pattern and emissions were affected by traffic characteristics, driver behavior (constant acceleration and deceleration), and vehicle characteristics. The air-fuel ratio (A/F) was found to correlate highly with emission rates, followed by acceleration/deceleration and speed. Further analysis found that more than 70% of the aggregated emissions were due to acceleration and deceleration, which contributed to nearly 70% of the travel time. This was followed by the breakdown of speed and emissions into different bins, which found that 20-30 kmph has a higher emission rate and 40-50 kmph bin has a lower emission rate.

Simulation of Signalized Intersection with Non-Lane-Based Heterogeneous Traffic Conditions Using Cellular Automata

Intersections affect the maneuvering and driving behavior of vehicles. The present study attempts to simulate an isolated signalized intersection with the dimensions obtained through the influence zone of intersections. This model includes several unexplored traffic characteristics observed at the intersection, such as non-lane-based heterogeneity and seepage behavior. The model was calibrated and validated with the field data collected in New Delhi, India. Several measures of performance, such as GEH statistics, Theil’s coefficient, root mean square error, and so forth, were used to validate and benchmark the simulation model. After calibration and validation, the model was used to find delays. The delays obtained from the model, several manuals, and the field were compared and found to be close to the field delays. Further, delays obtained from Indonesian and Canadian manuals were comparatively closer to the delays obtained from the field, whereas delays obtained from the Indian Highway Capacity Manual (2017) and U.S. Highway Capacity Manual (2010) are overestimated. The model presented can be used to benchmark the performance of signalized intersections under a variety of traffic and environmental conditions.

Evaluation of optimal route selection for public transport network routes on urban roads using Fuzzy‐TOPSIS method

AbstractIn developing countries such as India, Bengaluru city has experienced extraordinary growth in vehicle traffic on arterial roads, causing intersections to operate over capacity under mixed traffic conditions. The speed on urban streets in central business district areas is lower than 15 kmph during peak hours. One of the main routes connecting the airport to the central business district is also vulnerable to this problem. Therefore, providing an alternative route to divert traffic from the mainstream is critical. This study's main goal is to identify alternate routes from Majestic to the airport using remote sensing and a geographic information system, and then evaluate those alternates using the Fuzzy‐TOPSIS approach. This study has used the GIS Arc software application to select the alternative while taking numerous factors such as time, speed, distance, environmental impact, and economic feasibility into consideration. The video graphic and moving car observer methods have been used for the extraction of data for the inventory of the existing route, while the geo image processing tool has been used to analyze alternative routes in the Bengaluru metropolitan area (BMA). The analysis has shown that the alternative route has a lesser travel time of 2 h 30 min in public transit and 1 h 30 min in private mode. The future scope of the study is to compare the alternative and the most suitable routes to divert traffic. Another public transport route network could be developed to minimize the external incoming vehicles in BMA, particularly to reach the airport on urban roads under heterogeneous traffic conditions.

Modeling car and heavy commercial vehicle crashes on two-lane rural highways using the Poisson-Tweedie regression approach

This article develops vehicle type–based crash-prediction models for cars and heavy commercial vehicles (HCVs) as a function of the curve geometry and vehicle-based design consistency criteria under heterogeneous traffic conditions on two-lane, two-way rural highways, specifically in hilly terrains. A National Highway (NH-953) connecting Netrang and Rajpipla in India was selected. There are 38 curves in the study section, each having a different curve geometry. Speed data were collected using the radar gun for cars and HCVs. The geometric design consistency was evaluated using Criterion I (the difference between operating and design speeds). The results show that 53% of the curves for cars have good consistency, compared to 32% and 29% of the curves for HCVs, which have fair and poor consistency, respectively. The Poisson-Tweedie regression technique, which provides a unified framework to model over-dispersed, under-dispersed, zero-inflated, count-data, and multiple-response variables, was used to develop the crash prediction models. The results revealed that crashes (cars and HCVs) decrease as the curve radius, deflection angle, and length increase. Similarly, as the tangent length increases, the difference between operating and design speeds increases, making inconsistent highway alignment, resulting in increased chances of crashes. The results of the present study can help highway authorities to evaluate highway alignment consistency and develop corresponding proactive strategies to improve highway safety.

Realistic Approach for Capacity Estimation of U-Turns Under Heterogeneous Traffic Condition

The main purpose of the present study is to estimate U-turn capacity at mid-block median openings (MBMO). Estimation of U-turn capacity requires three key parameters, namely: conflicting traffic volume, critical gap, and follow-up time. Conflicting traffic volume has been estimated by introducing a novel parameter, namely, spatiotemporal conflicting factor. Critical gap has been estimated using five different methods: modified Raff method, Ashworth method, logit model, occupancy time method, and support vector machine technique. The follow-up time has been estimated directly from recorded videos. Subsequently, capacity has been estimated for different types of vehicles along with the U-turning stream capacity. The estimated capacity has been validated using field capacity, using Kyte’s method. Further, a suitable method for the estimation of critical gap in the Indian context has been determined. This study could be used as a guideline for the U-turn capacity estimation for efficient design and management of traffic at MBMO.

Heterogeneous road traffic noise modeling at mid-block sections of mid-sized city in India.

This study attempted to develop a computer-based software for monitoring the traffic noise under heterogeneous traffic condition at the morning peak (MP), off peak (OP), and evening peak (EP) periods of mid-block sections of mid-sized city in India. Traffic noise dataset of 776 (LAeq, 1hr) were collected from 23 locations of Gorakhpur mid-sized city in the state of Uttar Pradesh in India. K-nearest neighbor (K-NN) algorithm was adopted for traffic noise prediction modeling. Moreover, principal component analysis (PCA) technique was used for the dimensionality reduction and to overcome the problem of multi-collinearity. The developed model exhibits R2 value of 0.81, 0.78, and 0.77 in the MP, OP, and EP, respectively, forLeq, and a value of 0.86, 0.80, and 0.84 for L10. The proposed model can predict more than 94% observations within an accuracy of ±3%. Ultimately, a user-friendly noise level calculator named "Traffic Noise Prediction Calculator for Heterogeneous Traffic (TNPC-H)" was developed for the benefit of field engineers and policy planners.

LEVEL OF SERVICE ANALYSIS OF PEDESTRIAN CROSSING FACILITIES AT SIGNALIZED INTERSECTIONS UNDER HETEROGENEOUS TRAFFIC CONDITIONS

While designing roadway facilities the pedestrian crosswalks must be given high importance, because these are the locations where pedestrians interact with vehicular traffic directly. This study aims to determine Pedestrian Perception values (PV) and assess the Level of Service (LOS) for crosswalk facilities provided for pedestrians at signalized intersections in heterogeneous traffic conditions. For this purpose, 24 crosswalks of six signalized intersections were chosen: 16 crosswalk locations were used for model development and eight crosswalk locations were used for model validation. The perception value of pedestrians was collected by conducting a questionnaire survey and traffic data was collected using a videographic survey. The parameters considered for the present study are: pedestrian volume, time is taken by the pedestrian to cross the crosswalk, length of crosswalk, pedestrian crossing speed, vehicular volume, and turning movements. The present research work proposed a mathematical model for Perception Value (PV) to assess the LOS of crosswalks of signalized intersections in urban areas. The results of the study are helpful in enhancing pedestrian safety and comfort at crosswalks.