Effects of connected automated vehicle on stability and energy consumption of heterogeneous traffic flow system

With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)will coexist.In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system,we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference(FVD)model.Subsequently,adopting the linear stability theory,we propose the linear stability condition for the model through using the small perturbation method,and the validity of the heterogeneous model is verified by comparing with the FVD model.Through nonlinear theoretical analysis,we further derive the KdV-Burgers equation,which captures the propagation characteristics of traffic density waves.Finally,by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow,the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated.Subsequent analysis reveals emergent traffic phenomena.The experimental findings demonstrate that as CAV permeability increases,the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation,leading to enhanced stability of the traffic system.Furthermore,higher initial traffic density renders the traffic system more susceptible to congestion,resulting in local clustering effect and stop-and-go traffic phenomenon.Remarkably,the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing.Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow.This research contributes to providing theoretical guidance for future CAV applications,aiming to enhance urban road traffic efficiency and alleviate congestion.

Characteristics of lateral vehicular interactions in heterogeneous traffic with weak lane discipline

Heterogeneous traffic conditions prevail in developing countries. Vehicles maintain weak lane discipline which increases lateral interactions of vehicles significantly. It is necessary to study these interactions in the form of maintained lateral gaps for modeling this traffic scenario. This paper aims at determining lateral clearances maintained by different vehicle types while moving in a heterogeneous traffic stream during overtaking. These data were collected using an instrumented vehicle which runs as a part of the stream. Variation of obtained clearance with average speed of interacting vehicles is studied and modeled. Different instrumented vehicles of various types are developed using （1） ultrasonic sensors fixed on both sides of vehicle, which provide inter-vehicular lateral distance and relative speed; and （2） GPS device with cameras, which provides vehicle type and speed of interacting vehicles. They are driven on different roads in six cities of India, to measure lateral gaps maintained with different interacting vehicles at different speeds. Relationships between lateral gaps and speed are modeled as regression lines with positive slopes and beta-distributed residuals. Nature of these graphs （i.e., slopes, intercepts, residuals） are also evaluated and compared for different interacting vehicle-type pairs. It is observed that similar vehicle pairs maintain less lateral clearance than dissimilar vehicle pairs. If a vehicle interacts with two vehicles （one on each side） simultaneously, lateral clearance is reduced and safety of the vehicles is compromised. The obtained relationships can be used for simulating lateral clearance maintaining behavior of vehicles in heterogeneous traffic.

Modeling the heterogeneous traffic flow considering the effect of self-stabilizing and autonomous vehicles

With the increasing maturity of automatic driving technology,the homogeneous traffic flow will gradually evolve into the heterogeneous traffic flow,which consists of human-driving and autonomous vehicles.To better study the characteristics of the heterogeneous traffic system,this paper proposes a new car-following model for autonomous vehicles and heterogeneous traffic flow,which considers the self-stabilizing effect of vehicles.Through linear and nonlinear methods,this paper deduces and analyzes the stability of such a car-following model with the self-stabilizing effect.Finally,the model is verified by numerical simulation.Numerical results show that the self-stabilizing effect can make the heterogeneous traffic flow more stable,and that increasing the self-stabilizing coefficient or historical time length can strengthen the stability of heterogeneous traffic flow and alleviate traffic congestion effectively.In addition,the heterogeneous traffic flow can also be stabilized with a higher proportion of autonomous vehicles.

Performance-based intersection layout under a flyover for heterogeneous traffic

Flyovers are constructed to manage heavy through movement. However, traffic operations underneath a flyover remain unmanaged and often pose a major con- cern in developing countries with non-lane-based hetero- geneous traffic. This may reduce the overall benefit of a flyover. An alternative intersection layout is proposed to improve traffic operations at the intersection underneath a flyover. The proposed layout segregates the traffic move- ments through effective channelization. A traffic island is also proposed in the middle of the intersection to facilitate concurrent right-turning movements. This layout helps in eliminating a signal phase and cuts down traffic cycle time by 40 %. A microsimulation-based traffic simulation model is developed for the evaluation of the proposed layout. The simulation model demonstrates effectiveness of the proposed layout. Average delay and average queue length are compared to measure the effectiveness. Traffic volume sensitivity analysis is conducted to estimate the capacity of the proposed layout. An intersection underneath a flyover along the Eastern Expressway in Mumbai is considered for the case study. The effectiveness of the proposed layout at the study location for varying flow level is evaluated by comparing average delay, average stop delay, average number of stops per vehicle, average queue length, and maximum queue length.

Mathematical Validation of Proposed Machine Learning Classifier for Heterogeneous Traffic and Anomaly Detection

The modeling of an efficient classifier is a fundamental issue in automatic training involving a large volume of representative data.Hence,automatic classification is a major task that entails the use of training methods capable of assigning classes to data objects by using the input activities presented to learn classes.The recognition of new elements is possible based on predefined classes.Intrusion detection systems suffer from numerous vulnerabilities during analysis and classification of data activities.To overcome this problem,new analysis methods should be derived so as to implement a relevant system to monitor circulated traffic.The main objective of this study is to model and validate a heterogeneous traffic classifier capable of categorizing collected events within networks.The new model is based on a proposed machine learning algorithm that comprises an input layer,a hidden layer,and an output layer.A reliable training algorithm is proposed to optimize the weights,and a recognition algorithm is used to validate the model.Preprocessing is applied to the collected traffic prior to the analysis step.This work aims to describe the mathematical validation of a new machine learning classifier for heterogeneous traffic and anomaly detection.

Capacity drop through reaction times in heterogeneous traffic

The capacity drop forms a major reason why the prevention of congestion is targeted by traffic management, as lower capacities are detrimental to traffic throughput. Various reasons describing the dynamics behind the capacity have been described, however one of these, reaction times, has had less explicit attention when modelling on a macroscopic flow level. In this contribution, a method to include the effect of reaction times for the capacity drop in heterogeneous traffic is proposed. The applied method further overcomes difficulties in including reaction times in a discrete time model through relaxation of the updating process in the discretization. This approach is novel for application in the considered first order approach, which is practise ready, contrary to many other models that propose similar approaches. The combination of the introduced method and the model form a solid development and method to apply the capacity drop based on this causation of the capacity drop. The results of the experiment case showed that the influence of traffic heterogeneity had a limited effect on the severity of the capacity drop, while it did influence the time of congestion onset. The influence of the reaction time on traffic showed greater capacity drop values for greater reaction time settings. The findings showed the method effective and valid, while the model application is also practise ready.

Influence of geometric design characteristics on safety under heterogeneous traffic flow

This paper focuses on analysing the influence of geometric design characteristics on traffic safety using bi-directional data on a divided roadway operated under heterogeneous traffic conditions in India. The study was carried out on a four lane divided inter-city highway in plain and rolling terrain. Statistical modelling approach by Poisson regression and Negative binomial regression were used to assess the safety performance as occurrence of crashes are random events and to identify the influence of the geometric design variables on the crash frequency. Negative binomial regression model was found to be more suitable to identify the variables contributing to road crashes. The study enabled better understanding of the factors related to road geometrics that influence road crash frequency. The study also established that operating speed has a significant contribution to the total number of crashes. Negative binomial models are found to be appropriate to predict road crashes on divided roadways under heterogeneous traffic conditions.

A methodology for calibration of traffic micro-simulator for urban heterogeneous traffic operations

Traffic micro-simulation is a widely accepted tool in many countries for the evaluation and assessment of alternative design schemes.However,for several developing countries,replicating heterogeneous,non-lane based traffic in a micro-simulation framework is gaining increased importance and still remains a challenge due to its complexity.The present study demonstrates a methodology to calibrate a traffic micro-simulation model giving due consideration to vehicle-class specific driver behavior in an urban Indian scenario for a midblock section and an intersection approach in Kolkata.The sensitive parameters affecting the driver behavior were identified for every vehicle type using Latin Hyper cube design,taking vehicle class specific travel time as a performance measure.Linear regression models were developed for each vehicle class considering the sensitive driving behavior parameters.The models highlight that the dependency of measure of effectiveness(MOE)of one vehicle type is not only limited to its own driver behavior but also on parameters of other vehicle classes.A genetic algorithm based optimization was adopted to obtain optimal parameter sets for different vehicle classes.The optimum values were found to vary significantly across all vehicle classes at a 95%confidence level.Single and multi-criteria calibration principles are also implemented to yield much more realistic results and subsequently minimizing weighted error for all vehicle classes.

Safety benefit of cooperative control for heterogeneous traffic on-ramp merging

The safety of heterogeneous traffic is a vital topic in the oncoming era of autonomous vehicles(AVs).The cooperative vehicle infrastructure system(CVIS)is considered to improve heterogeneous traffic safety by connecting and controlling AVs cooperatively,and the connected AVs are so-called connected and automated vehicles(CAVs).However,the safety impact of cooperative control strategy on the heterogeneous traffic with CAVs and human-driving vehicles(HVs)has not been well investigated.In this paper,based on the traffic simulator SUMO,we designed a typical highway scenario of on-ramp merging and adopted a cooperative control method for CAVs.We then compared the safety performance for two different heterogeneous traffic systems,i.e.AV and HV,CAV and HV,respectively,to illustrate the safety benefits of the cooperative control strategy.We found that the safety performance of the CAV and HV traffic system does not always outperform that of AV and HV.With random departSpeed and higher arrival rate,the proposed cooperative control method would decrease the conflicts significantly whereas the penetration rate is over 80%.We further investigated the conflicts in terms of the leading and following vehicle types,and found that the risk of a AV/CAV followed by a HV is twice that of a HV followed by another HV.We also considered the safety effect of communication failure,and found that there is no significant impact until the packet loss probability is greater than 30%,while communication delay’s impact on safety can be ignored according to our experiments.

Urban road segment level of service based on bicycle users' perception under mixed traffic conditions

During the past two decades, several methodologies are endorsed to assess the compatibility of roadways for bicycle use under homogeneous traffic conditions. However, these methodologies cannot be adopted under heterogeneous traffic where on-street bicyclists encounter a complex interaction with various types of vehicles and show divergent operational characteristics. Thus, the present study proposes an initial model suitable for urban road segments in mid-sized cities under such complex situations. For analysis purpose, various operational and physical factors along with user perception data sets （13,624 effective ratings in total） were collected from 74 road segments. Eight important road attributes affecting the bicycle service quality were identified using the most recent and most promising machine learning technique namely, random forest. The identified variables are namely, effective width of outside through lane, pavement condition index, traffic volume, traffic speed, roadside commercial activities, interruptions by unauthorized stoppages of intermittent public transits, vehicular ingress-egress to on-street parking area, and frequency of driveways carrying a high volume of traffic. Service prediction models were developed using ordered probit and ordered logit modeling structures which meet a confidence level of 95%. Prediction performances of developed models were assessed in terms of several statistical parameters and the ordered probit model outperformed the ordered logit model. Incorporating outputs of the probit model, a pre- dictive equation is presented that can identify under what level a segment is offering services for bicycle use. The service levels offered by roadways were classified into six categories varying from ＇excellent＇ to ＇worst＇ （A-F）.

Modeling differential car-following behavior under normal and rainy conditions: A memory-based deep learning method with attention mechanism

In order to analyze and learn the difference in car-following behavior between normal and rainy days, we first collect car-following trajectory data of an urban elevated road on normal and rainy days by microwave radar and analyze the differences in speed, relative speed, acceleration, space headway, and time headway among data through statistics. Secondly, owing to the time-series characteristics of car-following data, we use the long short-term memory(LSTM) neural network optimized by attention mechanism(AM) and sparrow search algorithm(SSA) to learn the different car-following behaviors under different weather conditions and build corresponding models(ASL-Normal, ASL-Rain, where ASL stands for AM-SSA-LSTM), respectively. Finally, the simulation test shows that the mean square error(MSE) and reciprocal of time-to-collision(RTTC) of the ASL model are better than those of LSTM and intelligent diver model(IDM), which is closer to the real data. The ASL model can better learn different driving behaviors on normal and rainy days. However,it has a higher sensitivity to weather conditions from cross test on normal and rainy data-sets which need classification training or sample diversification processing. In the car-following platoon simulation, the stability performances of two models are excellent, which can describe the basic characteristics of traffic flow on normal and rainy days. Comparing with ASL-Rain model, the convergence time of ASL-Normal is shorter, reflecting that cautious driving behavior on rainy days will reduce traffic efficiency to a certain extent. However, ASL-Normal model produces a more severe and frequent traffic oscillation within a shorter period because of aggressive driving behavior on normal days.