Proactive Platooning Based on C-V2X to Relieve Congestion at a Signalized Intersection

Nowadays urban traffic congestion becomes a major issue due to the increase in vehicles that lead to more commuting time,accidents,traffic violations,and high fuel consumption.Platooning is a good way to solve traffic congestion because it drives with a smaller inter-vehicle distance than human-driving.This paper proposes proactive platooning based on C-V2X(cellular vehicle-to-everything)to relieve congestion.Proactive platooning reduces the inter-vehicle distance and increases the throughput of signalized intersections through integrating the networked control platooning and computation-centralized platooning.Model and simulation experiments of proactive platooning were conducted to verify the impact of inter-vehicle distance on platooning latency,platooning safety,and traffic throughput.Simulation results show that the optimal inter-vehicle distance under proactive platooning is less than half of human-driving at a signalized intersection.

Estimation of Probability of Swarming Pedestrians' Violation at Signalized Intersections in Developing Cities

We made an on-site investigation about pedestrian violation of traffic signals at a signalized intersection in Xi＇an, Shaanxi province, China. Based on it, we studied the impact of pedestrian＇s waiting time on violation decision and the impact of the number of pedestrians in colony on the probability of swarming pedestrians＇ violation. The result revealed that the probability of pedestrian violation rose with the waiting time for the pedestrians＇ green signal. Then we developed a Monte Carlo model for simulating mixed vehicles and pedestrians and used the on-site investigation data to validate the model. When traffic volume is fight, the error between the simulated values and the measured ones is 2.67%. When traffic volume is heavy, the error is 3.38%.

Examining the Risk Factors of Rear-End Crashes at Signalized Intersections

Rear-end crashes are among the most common crash types at signalized intersections. To examine the risk factors for the occurrence of this crash type, this study involved the analysis of nine years of intersection crash records in the state of Wyoming. With that, the contributing factors related to crash, driver, environmental, and roadway characteristics, including pavement surface friction, were investigated. A binomial logistic regression modeling approach was applied to achieve the study’s objective. The results showed that three factors related to crash and driver’s attributes (commercial vehicle involvement, speeding, and driver’s age) and four factors related to environmental and roadway characteristics (lighting, weather conditions, area type, whether urban or rural and pavement friction) are associated with the risk of rear-end crash occurrence at signalized intersections. This study provides insights into the mitigation measures to implement concerning rear-end crashes at signalized intersections.

Temporal and spatial compliance behaviour of pedestrians under the influence of time pressure at signalized intersections:A pedestrian simulator study

Pedestrian safety is at high stakes due to the non-compliance practices of pedestrians at signalized intersections.Additionally,when pedestrians are hurrying,they deliberately engage in such unsafe behaviour.Therefore,the purpose of this study was to understand how time pressure(i.e.,feeling of hurry or saving time)affected pedestrians'decisions to follow traffic rules at signalized junctions.To achieve the study objectives,a pedestrian simulator setup was used to collect the crossing behaviour of forty participants at a four-legged signalized intersection.Non-compliance,one of the riskiest pedestrian behaviours,was examined with respect to three different forms,comprising dangerous temporal non-compliance(D-TNC),non-dangerous temporal non-compliance(ND-TNC),and spatial non-compliance(SNC)behaviour under two distinct conditions:baseline(i.e.,no time pressure)and time pressure conditions.The effects of demographics,usual walking features,and time pressure on D-TNC and ND-TNC were investigated using a multinomial regression model,while SNC behaviour was investigated using a binary regression model.It was interesting to note that the majority of the factors related to pedestrians’usual walking behaviour had an impact on all kinds of non-compliance behaviours.Importantly,the results also showcased that time pressure had a contrasting impact on D-TNC and ND-TNC behaviour whereas SNC behaviour increased under time pressure.Additionally,the varying impacts of D-TNC,ND-TNC,and SNC were also reflected in the occurrence of the crashes,which were probably triggered by discrepancies in the influence of time pressure on non-compliance behaviours.These findings highlight the need for technical solutions,educational outreach,and efficient enforcement practices to reduce pedestrians'non-compliant behaviour.

Influence of Bicycle Traffic on Capacity of Typical Signalized Intersection

Bicycle traffic has a significant effect on the capacity of signalized intersections. This paper divides the influence of bicyclists on vehicular flow into four types with the time durations estimated based on probability, shock wave, and gap acceptance theory. Vehicular saturation flow rate is predicted for various conditions on the basis of the speed-flow curve for the capacity of typical intersections influenced by bicycle traffic The model overcomes the limitations of the Highway Capacity Manual （HCM, 2000） method for left-turns due to data collection, and takes into account the effect of trapped bicycles on the through vehicular traffic. The numerical results show that the left-turn and through capacities predicted by the model are lower than those of the HCM method. The right-turn capacity is close to that of the HCM method at low bicycle volumes and higher than that of the HCM method at high bicycle volumes.

Study on pedestrian crossing behavior at signalized intersections

A clear understanding of pedestrian crossing behavior under mixed traffic conditions is needed for providing necessary infrastructure and also for enhancing pedestrian safety at signalized intersections. This paper attempts to analyze the crossing behavior of pedestrians like crossing speed, compliance with signal, and pedestrian-vehicular interaction under mixed traffic conditions and to iden- tify the influencing factors based on statistical tests. 775 pedestrian samples were observed from three signalized intersections in Mumbai, India for analyzing crossing behaviors and the significant factors affecting traffic signal compliance by pedestrians were identified by conducting Pearson＇s correlation co- efficient test, ANOVA test, and Student t test. Factors influencing pedestrian crossing speed had been studied and a design crossing speed had been determined for old and adult pedestrians at 0.95 m/s and 1.12 rn/s respectively. Logistic regression models had been developed in which the odds of pedestrian violation and interactions were modeled and verified. This study can help researchers and practitioners to understand pedestrian crossing behavior at signalized intersections and develop pedestrian delay mod- els under mixed traffic conditions.

Delay Estimates of Mixed Traffic Flow at Signalized Intersections in China

Two characteristics of Chinese mixed traffic invalidate the conventional queuing delay estimates for western countries. First, the driving characteristics of Chinese drivers lead to different delays even though the other conditions are the same. Second, urban traffic flow in China is often hindered by pedestrians at intersections, such that imported intelligent traffic control systems do not work appropriately. Typical delay estimates for Chinese conditions were obtained from data for over 500 vehicle queues in Beijing collected using charge coupled device （CCD） cameras. The results show that the delays mainly depend on the pro- portion and positions of heavy vehicles in the queue, as well as the start-up situations （with or without interference）. A simplified delay estimation model considers vehicle types and positions that compares well with the observed traffic delays.

Safety impacts of red light running photo enforcement at urban signalized intersections

Red light running at signalized intersections is a major safety concern in the United States. Statistics show that approximately 45 percent of crashes at intersections caused by red light running re- sult in severe injuries and fatalities, while only approximately 30 percent of all other types of intersec- tion crashes cause injuries or fatalities. Over the past decade, many US cities and counties have de- ployed red light running photo enforcement systems for signalized intersections within their jurisdictions to potentially reduce red light running related crashes. This study proposes an empirical Bayesian （ EB ） before-after analysis method that computes a weighed sum of crashes observed in the field and crashes predicted by safety performance functions （SPFs） to mitigate regression-to-mean biases for analyzing crash reduction effects of red light running enforcement. The analysis explicitly considers red light run- ning related crash types, including head-on, rear-end, angle, tuming, sideswipe in the same direction, and sideswipe in the opposite direction; and crash severity levels classified as fatal, injury, and proper- ty damage only （PDO）. A computational study is conducted to examine the effectiveness of the Chica- go program with red light running photo enforcement systems deployed for nearly two hundred signal- ized intersections. It is revealed that the use of red light running photo enforcement on the whole is pos- itive, as demonstrated by reductions in all types of fatal crashes by 4-48 percent, and injury-related an- gle crashes by 1 percent. However, it slightly raises PDO-related angle crashes and moderately increa- ses injury and PDO related rear-end crashes. The safety effectiveness of red light running photo en- forcement is sensitive to intersection location.

Capacity Reliability of Signalized Intersections with Mixed Traffic Conditions

The reliability of capacity of signalized intersections in mixed traffic conditions involving vehicles, bicycles, and pedestrians was investigated to complete the conventional, deterministic capacity calculations. Simulations using VISSIM provided estimates of capacity distributions, and demonstrated the effects of the analysis intervals on the distributions. With the random vehicle arrivals taken into account, a capacity reliability assessment method was given as a function. Assessments were also performed regarding the effects of the conflicting pedestrian and bicycle volumes on capacity reliability. The simulation indicates that the pedestrians and bicycles result in greater random fluctuations of exclusive turning lane capacities, but have less effect on the variability of shared lane capacities. Normal distributions can be used to model the capacities for intervals not less than 10 rain. At higher vehicular volumes, the capacity reliability is more sensitive to the mean and standard deviation of the pedestrian and bicycle volumes.

Analysis of delay variability at isolated signalized intersections

On urban arterials,travel time variability is largely dependent on the variability in the delays vehicles experience at signalized intersections.The interpretation of delay evolvement at intersections will give a comprehensive insight into arterial travel time variability and provide more possibilities for travel time estimation.Accordingly,an analytical model is proposed to study delay variability at isolated,fixed-time controlled intersections.Classic cumulative curves are utilized to derive average delay(per cycle) formulas by assuming a deterministic overflow queue.Then,an analogy with the Markov chain process is made to clarify the mechanism of stochastic delays and overflow queues at signalized intersections.It was found that,in undersaturated cases,the shape of the delay distribution changes very little over time,whereas for saturated and oversaturated cases the delay distribution is time-dependent and becomes flatter with an increasing number of cycles.The analysis of arrival distributions,e.g.,Poisson and binomial,produces the conclusion that the variability of arrivals has a significant effect on delay estimates in both undersaturated and oversaturated conditions.A larger variance of arrival flow results in a larger variance of delay distribution.All of these analyses can help road authorities to gain insights into arterial travel time variability.

Identification and Classification of Driving Behaviour at Signalized Intersections Using Support Vector Machine

When the drivers approaching signalized intersections(onset of yellow signal),the drivers would enter into a zone,where they will be in uncertain mode assessing their capabilities to stop or cross the intersection.Therefore,any improper decision might lead to a right-angle or back-end crash.To avoid a right-angle collision,drivers apply the harsh brakes to stop just before the signalized intersection.But this may lead to a back-end crash when the following driver encounters the former's sudden stopping decision.This situation gets multifaceted when the traffic is heterogeneous,containing various types of vehicles.In order to reduce this issue,this study's primary objective is to identify the driving behaviour at signalized intersections based on the driving features(parameters).The secondary objective is to classify the outcome of driving behaviour(safe stopping and unsafe stopping)at the signalized intersection using a support vector machine(SVM)technique.Turning moments are used to identify the zones and label them accordingly for further classification.The classification of 50 instances is identified for training and testing using a 70%-30% rule resulted in an accuracy of 85% and 86%,respectively.Classification performance is further verified by random sampling using five cross-validation and 30 iterations,which gave an accuracy of 97% and 100% for training and testing.These results demonstrate that the proposed approach can help develop a pre-warning system to alert the drivers approaching signalized intersections,thus reducing back-end crash and accidents.

Queue clearance rate method for estimating passenger car equivalents at signalized intersections

This study explored the use of queue clearance rate method for estimating passenger car equivalent （PCE） at signalized intersections. PCE was estimated based on the assumption that the rate at which a queue of vehicles clears the intersection is a function of its composition. Results of this method were compared with the results estimated by some popular techniques. A fourqegged intersection was simulated in VISSIM software and different techniques were used to convert the traffic mix into a uniform one. Parameters of VISSIM were modified to closely reflect the traffic behaviour under heterogeneous traffic conditions. All approaches of the intersection were loaded to saturated conditions and accuracy of estimated PCEs were established by comparing converted flow （PCE/h） with the capacity of an all-car traffic stream. Method based on saturation flow delivered the best result, but its use was limited to traffic composed only of two types of vehicles. Results of regression and optimization techniques were almost similar and the converted flow was close to the capacity of all-car stream. However, accuracy of these methods strongly relied on the correct measurement of saturation flow. Queue clearance rate method did not require value of saturation flow and yielded good estimates of PCE throughout the simu- lation runs. The maximum difference between the converted flow and capacity estimated with all car situations was found to be less than 10% in all cases considered in this study.

Individual variable speed limit trajectory planning considering stochastic arriving patterns

Connected vehicles enabled by communication technologies have the potential to improve trafc mobility and enhance roadway safety such that trafc information can be shared among vehicles and infrastructure.Fruitful speed advisory strategies have been proposed to smooth connected vehicle trajectories for better system performance with the help of diferent carfollowing models.Yet,there has been no such comparison about the impacts of various car-following models on the advisory strategies.Further,most of the existing studies consider a deterministic vehicle arriving pattern.The resulting model is easy to approach yet not realistic in representing realistic trafc patterns.This study proposes an Individual Variable Speed Limit(IVSL)trajectory planning problem at a signalized intersection and investigates the impacts of three popular car-following models on the IVSL.Both deterministic and stochastic IVSL models are formulated,and their performance is tested with numerical experiments.The results show that,compared to the benchmark(i.e.,without speed control),the proposed IVSL strategy with a deterministic arriving pattern achieves signifcant improvements in both mobility and fuel efciency across diferent trafc levels with all three car-following models.The improvement of the IVSL with the Gipps’model is the most remarkable.When the vehicle arriving patterns are stochastic,the IVSL improves travel time,fuel consumption,and system cost by 8.95%,19.11%,and 11.37%,respectively,compared to the benchmark without speed control.

Optimization of traffic signal parameters based on distribution of link travel time

In order to make full use of digital data, such as data extracted from electronic police video systems, and optimize intersection signal parameters, the theoretical distribution of the vehicle's road travel time must first be determined. The intersection signal cycle and the green splits were optimized simultaneously, and the system total travel time was selected as the optimization goal. The distribution of the vehicle's link travel time is the combined results of the flow composition, road marking, the form of control, and the driver's driving habits. The method proposed has 15% lower system total stop delay and fewer total stops than the method of TRRL(Transport and Road Research Laboratory) in England and the method of ARRB(Australian Road Research Board) in Australia. This method can save 0.5% total travel time and will be easier to understand and test, which establishes a causal relationship between optimal results and specific forms of road segment management, such as speed limits.

Evaluation and Effectiveness of Reallocating Traffic Signal Timing at Corridors with High Freight Volume

The COVID-19 pandemic drastically altered traffic patterns across the globe.With more people staying at home and relying on delivery services,there was a reduction in passenger car traffic but an increase in freight traffic.This study investigates the changes in traffic volume and mode splits during the COVID-19 pandemic at the intersection of Broening Highway and Holabird Avenues and investigates traffic signal reallocation strategies that account for the changes in traffic.This study considers morning peak traffic volumes before and during COVID.A methodology was developed to evaluate and measure freight volumes and emissions.From the literature,the cost per kilogram of CO,VOC,and NOx was$5.85,$3,37,and$12.53,respectively.The intersection operated at a level of service(LOS)of C both pre-COVID and during COVID.Traffic volume decreased by 24.3%during COVID at the study location;car volumes declined by 50%,and truck volumes increased by 130%.The total rate of emissions decreased by 22.6%.

Estimation of Incident-Induced Congestion on Signalized Arteries Using Traffic Sensor Data

This paper describes how to derive quantitative information about the effect of traffic incidents on urban traffic flow from the raw measurements detected by sectional loop sensors. 3-he two critical parame- ters of the travel delay and queue length, which reflect the temporal and spatial properties of inci- dent-induced congestion, cannot be directly determined from commonly used loop sensors. The modified queuing diagram is used here to quantify incident-induced queues and travel delays on signalized arteries using sensor data from the targeted and upstream links. The method is tested using data generated by the VISSIM simulation model, with results indicating its efficiency and accuracy with an average relative travel delay error of 18.67% for all samples which falls to 8.07% for high volume and very high volume conditions.

Impact of intersection type and a vehicular fleet's hybridization level on energy consumption and emissions

A vehicle＇s energy consumption and emissions are two major constraints in sustainable development. Both of them have proportionally raised in recent decades with the exponential growth of world traffic demands. The reduction of road traffic-generated energy consumption and emissions have thus become unprecedentedly challenging and worth examining. This paper investigates energy consumption and environmental problems present at roundabout and signalized intersection to analyze the impact of the hybridization level＇s fleet and intersection type on vehicle consumption and pollution. Instantaneous fuel consumption and emission models coupled with simulation of urban mobility （SUMO） are in this study. The authors started with modeling energy consumption. Then, an emission model emissions from traffic （EMIT） was implemented to quantify vehicle emissions of CO2, CO and NOX. These models help investigate the influence of intersection type on energy consumption and environmental conditions. The authors implemented a signalized intersection and roundabout using SUMO. The input data are collected from the roundabout of Sousse （Tunisia） using video data collection. Since there is a lack of econometric models that emulate hybridized stream behavior near intersections, two energy consumption models for the roundabout and crossroad are developed using traffic flow and hybridization level as the input variables. Compared to crossroads, a roundabout can obtain more environmental improvements and substantial reductions in energy consumption and road traffic emissions.

Yellow light dilemma zone researches: a review

The yellow light dilemma zone is widely known as an area on the high-speed intersection ap- proach, where vehicles neither safely stop before the stop line nor proceed through the intersection dur- ing amber interval. Within such an area, a vehicle might be involved in a right-angle crash or rear-end collision. This issue has been extensively discussed over five decades in traffic engineering field, cov- eting from theory to practice. However, few comprehensive review literatures on the amber signal di- lemma zone problems can be found. The objective of this paper is to summarize the evolution of yellow light dilemma zone researches. Basic definition and boundary of dilemma zone followed by driver be- havior and dilemma zone hazard measurement are depicted. At last, the future directions of yellow light dilemma zone research are discussed.

Comparison of Vehicle Departure Headways in Beijing and Atlanta

Two characteristics of traffic flows make the vehicle departure headways in Beijing, China different from that in Atlanta, USA. Urban traffic flows in China are often hindered by pedestrians at intersections and queuing vehicles often cannot be totally discharged during the green time, so most signalized intersections are over-saturated in the day time. The first characteristic makes the departure headways of the first and second vehicles in Beijing depend heavily on the start-up situations （with or without interference）. The departure headways of the 3rd to 5th vehicles in Beijing have similar characteristics to those in Atlanta. The second characteristic makes the departure headways after the 5th vehicle significantly different from those in Atlanta. These comparisons show that the conventional delay estimate methods and signal control algorithms may not fit Chinese scenarios, which makes imported intelligent traffic control systems not work well in China without calibrations.