Mixed Traffic Situations Research Articles

Congested traffic patterns of mixed lattice hydrodynamic model combining the perceptual range differences with passing effect

With the aid of Vehicle-to-everything (V2X) technologies for network connectivity, connected autonomous vehicles (CAVs) can broaden the drivers' perceptual boundaries and receive a greater quantity of exogenous vehicle information, thereby governing the vehicle's acceleration information of the next moment. Nonetheless, constrained by contemporary communication networks and sophisticated vehicle control technology, the process of promoting CAVs is long-lasting, and throughout this stage of transition, both CAVs and human-driven vehicles (HDVs) will happen on the road. Moreover, passing behaviour is uncommon in the traffic flow model despite being a fundamental microscopic driving behaviour, particularly within mixed-traffic situations. To bridge that hole, we introduce the percentage ratios of CAVs into the lattice hydrodynamic model by integrating the perceptual range differences between two different types of vehicles with passing effects. Subsequently, the stability norm associated with the new model is ascertained by performing the linear stability analysis. When the above-mentioned stability condition is not achieved, we investigate the complex behaviour of the new model, and the associated existing conditions, as well as the modified Korteweg-de Vries (mKdV) equation, are determined simultaneously. When the passing ratio is inadequate, no jam and kink jam make up the whole phase region; while when the passing ratio surpasses the minimum, the initial unstable region can be segregated into two extra segments: the chaotic sub-region and the kink jam sub-region, the density wave progressively transitioned from being a kink-Bando traffic wave to a chaotic phase. Lastly, the findings of the numerical experiment identify with the theoretical derivation made above.

A modeling approach for suitability evaluation of traffic noise prediction under mixed traffic situation in mid-sized Indian cities

A modeling approach for suitability evaluation of traffic noise prediction under mixed traffic situation in mid-sized Indian cities

Interpreting pedestrians' head movements when encountering automated vehicles at a virtual crossroad

In the future, Automated Vehicles (AVs) may be able to use pedestrians’ head movement patterns to understand their crossing intentions. This ability of the AV to predict pedestrian crossing intention will improve road safety in mixed traffic situations and may also enhance traffic flow, allowing the vehicle to gradually reduce its speed in advance of a yield, eliminating the need for a complete and erratic halt. To date, most of the work conducted on studying pedestrian head movements has been based on observation studies. To further our understanding in this area, this study examined pedestrians' head movements when interacting with AVs during a range of road crossing scenarios, developed in a VR environment. Thirty-eight participants took part in this CAVE-based pedestrian simulator study. Head movements were recorded using stereoscopic motion-tracking glasses, as pedestrians crossed the road in response to an AV which approached from the right (UK-based road). A zebra crossing was included in half of the trials to understand how it affected crossing behaviour. The effect of different approaching speeds of the AV, and the presence of an external Human-Machine Interface (eHMI), on head movements and crossing behaviour was also studied. Results showed that the absolute head-turning rate (change in pedestrians' head-turning angle, per frame) increased significantly at around 1 s before a crossing initiation, reaching a peak at the crossing initiation, where pedestrians presented a “last-second check” before the crossing decision. Another increase in absolute head-turning rate to the right was seen at the end of the crossing (∼1.5 s after crossing initiation), to check the proximity of the approaching vehicle. A higher rate of head-turning was also seen for AV-non-yielding scenarios. Finally, the least number of head turns was seen for the yielding conditions which included an eHMI, in the presence of the zebra crossing. These results show the value of infrastructural and vehicle-based cues in assisting pedestrians’ crossing decisions and provide an insight into how head-turning behaviour can be used by AVs to better predict pedestrians’ crossing intentions in urban settings.

Prospects and Challenges of E-Rickshaws in Urban Transportation Systems of Bangladesh: A Case Study of Rangpur City Corporation

The availability of public transportation is frequently insufficient and the quality of the services is quite low in many cities of developing country. This is mostly because of the growing discrepancy between the supply and demand for transportation infrastructure and the provision of services. As a result, E-rickshaws (battery-operated electric three-wheelers locally termed as Auto/Easy bike) have emerged as a form of para-transit/informal public transport in many developing country cities and providing flexible and demand-responsive services to users. E-rickshaws are becoming one of the main travel modes and playing a crucial role in urban mobility because bus-based or formal public transportation is lacking in many medium- and small-sized cities in Bangladesh. This study made an effort to answer how E-rickshaws might function in the future when public bus service will be launched in a mixed traffic situation of Bangladesh. Rangpur in Bangladesh was selected as the representative of developing country cities for conducting the case study. This study is generally based on primary survey because the E-rickshaw is a new concept in urban transport. Primary data were collected at different road intersections and some temporary E-rickshaw stands in Rangpur City (as no permanent E-rickshaw stand in the city). Basically, the data was collected by Questionnaires Survey with E-rickshaw Drivers and Users; Focus Group Discussion (FGD) with E-rickshaw union members and Key Informants Interview (KII) with local authorities who are concerned with E-rickshaws. The driver-owned, operational E-rickshaw has an income-investment ratio of 1.45 and generates an average daily income of BDT 850. According to the survey, 81% of the drivers of E-rickshaws agreed that their current financial situation or income is better than it was when they worked in another or previous occupation. Compared with other travel modes, commuters are very satisfied with the quality of E-rickshaw services. Based on study findings it may be concluded that the E-rickshaw is a potential future-proof means of transportation. Public transport planning should include E-rickshaw in providing services which may not be catered by the formal public transport. The conclusions drawn from this case study is that locations may be applied to other places in the country or cities where rickshaws are a significant source of informal public transportation.

An investigation of traffic speed distributions for uninterrupted flow at blackspot locations in a mixed traffic environment

Modelling traffic characteristics is the foundation for resolving various traffic and transportation issues. Among them, traffic speed has a significant impact on roadway crashes at blackspot (BS) locations. Speed is a random variable; several studies have recommended normal distribution to characterize the distribution of traffic speed for uninterrupted flow. However, a mixed-traffic situation causes heterogeneity, and the distribution of speeds deviates from the normal distribution. The present study investigates the distributions of traffic speeds for uninterrupted flow at 18 blackspot locations and individual vehicle types in mixed-traffic environments. Seven distribution models, namely Normal, Lognormal, Gamma, Logistic, Weibull, Burr, and Generalized Extreme Value (GEV), are considered to determine the speed characteristics. Different parametric distribution models are fitted to the vehicular speeds using maximum likelihood estimation (MLE) methods. Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and two penalized criteria, i.e., Akaike and Bayesian Information Criteria (AIC and BIC), are used as goodness-of-fit (GoF) measures to find the best-fitting distribution. The overall suitability of each predicted distribution is also determined using a novel ranking method. The test findings suggest that GEV and Burr are the most suitable empirical speed distributions, with GEV fitting best above 96%. When the heavy vehicle composition (truck, bus, and tractor) is below 10%, 10–14%, 15–20%, and above 20%, it follows the Weibull, Gamma, GEV, and Burr distributions, respectively, in a mixed traffic environment.

Improving safety of interactions between conventional and autonomous ships

Automatic controllers work best when the system they control can be sufficiently well modelled. This is a problem for control of autonomous ships in mixed traffic situations where the autonomous ship interacts with conventional ships, as the crew on other ships can and will exert unexpected behaviour that cannot be easily modelled. This paper analyses the problem of information acquisition, situational assessment and how to predict other ship's actions for autonomous ships that need to interact with conventional ships. We identify causes for the interaction problem and classify these into a decision making model. We also identify possible measures to overcome the problems and based on an impact analysis where technical, procedural and regulatory aspects are considered, we discuss and propose some possible ways to reduce or solve this problem. The conclusion is that the most likely and effective short-term solution is to assist the autonomous ships with human operators and the best longer-term solution may be to improve the information exchange between the ships, complemented with changes in COLREGs.

Framework for Connected and Automated Bus Rapid Transit with Sectionalized Speed Guidance based on deep reinforcement learning: Field test in Sejong City

Nowadays, Automated Vehicle (AV) technology is gaining attention as a candidate to improve the efficiency of Bus Rapid Transit (BRT) systems. However, there are still some challenges in AV technology including limited perception range and lack of cooperation capability in mixed traffic situations with drivers. The emerging Connected and Automated Vehicles (CAVs) and Cooperative Intelligent Transportation System (C-ITS) offer an unprecedented opportunity to solve such challenges. As a result, this study presents a framework for Connected and Automated BRT (CA-BRT), including a cloud-based architecture and a deep reinforcement learning system for Sectionalized Speed Guidance (SSG) system designed for CAVs. The proposed framework is field-tested in Sejong City in South Korea, where there are various road environments such as bus stops, overpasses, underground tunnels, intersections, and crosswalks. The driving performance of the proposed system is compared with different types of control scenarios, and the results from the field tests show that the proposed system improves the driving performance of the AVs in various aspects including driving safety, ride comfort, and energy efficiency with downstream information obtained from road infrastructures.

The Impact of Traffic Information Provision and Prevailing Policy on the Route Choice Behavior of Motorcycles Based on the Stated Preference Experiment: A Preliminary Study

It is anticipated that the prevalence of motorcycles in Asian countries will continue to increase, causing congestion and network imbalances concerning the nature of motorcycles. Literature demonstrates Variable Message Signs (VMSs) as an effective measure for addressing this issue. Understanding route choice behavior may thus aid in determining the appropriate traffic information to broadcast. This study aims to identify the impact of VMS messages related to traffic conditions and regulations on the route choice of motorcycle riders. In this instance, the core concept of ramp metering is adapted for non-highways to manage the proportion of motorcycles entering the traffic stream of the mainline. Two predetermined routes were offered through a stated preference survey to capture the responses to VMS. A binary logit model was initially introduced, further improved by including the individual characteristics and accommodating the unobserved factors across a series of observations (panel effects) by applying the mixed binary logit. It was revealed that traffic flow conditions significantly affect route preference; therefore, motorcycles tend to choose routes with lower volumes. However, waiting time at a ramp meter has no impact. The present research is a preliminary investigation for further implications in proposing traffic management strategies under mixed traffic situations.

Is Only the Dedicated Lane for Automated Vehicles Essential in the Future? The Dedicated Lanes Optimal Operating System Evaluation

As new mobility called automated vehicles (AVs) appears on the road, positive effects are expected, but in fact, unexpected adverse effects may arise due to the mixed traffic situation with human-driven vehicles (HVs). Prior to the commercialization of AVs, a preliminary review and preventive measures are required, and among them, the interaction between the existing vehicle and the new mobility and the interaction with the infrastructure must be considered. Therefore, we propose (i) the positive–negative effect of introducing AVs in a mixed traffic situation and (ii) the optimal operation plan for the dedicated lane for AVs. First, the effect of introducing AVs considering the interaction between vehicles in the mixed traffic situation showed mostly positive such as speed increase, delay time reduction, and capacity increase. However, in a 75% Market Penetration Rate (MPR) environment of all levels of Service (LOS), the effect was diminished compared to the previous MPR. This is contemplated to be the result of a conflict caused by the operation of some HVs (including heavy vehicles) behavior as obstacles in the situation where most of the vehicles on the road are AVs. Based on the previous result, we deployed the dedicated lane to resolve the negative effect in the 75% MPR environment and proposed an optimal operation strategy for the AVs dedicated lane from the perspective of operational efficiency for a more feasible operation. Given the 75% MPR, the Mixed-Use operation strategy of High-Occupancy Vehicles (HOV) and AVs is ascertained as the most suitable operation strategy. This implies that even in the era of AVs, the influence of other vehicles (e.g., heavy vehicles, other mobility) must be considered. This study is significant by considering the negative effects of the introduction of AVs and presenting an optimal operation strategy for dedicated lanes, and it can expect to be used as a new strategy as part of the Free/Expressway Traffic Management System (FTMS) applicable in the era of autonomous driving.

Improved Real-Time Traffic Obstacle Detection and Classification Method Applied in Intelligent and Connected Vehicles in Mixed Traffic Environment

Mixed traffic is a common phenomenon in urban environment. For the mixed traffic situation, the detection of traffic obstacles, including motor vehicle, non-motor vehicle, and pedestrian, is an essential task for intelligent and connected vehicles (ICVs). In this paper, an improved YOLO model is proposed for traffic obstacle detection and classification. The YOLO network is used to accurately detect the traffic obstacles, while the Wasserstein distance-based loss is used to improve the misclassification in the detection that may cause serious consequences. A new established dataset containing four types of traffic obstacles including vehicles, bikes, riders, and pedestrians is collected under different time periods and different weather conditions in urban environment in Wuhan, China. Experiments are performed on the established dataset on Windows PC and NVIDIA TX2, respectively. From the experimental results, the improved YOLO model has higher mean average precision than the original YOLO model and can effectively reduce intolerable misclassifications. In addition, the improved YOLOv4-tiny model has a detection speed of 22.5928 fps on NVIDIA TX2, which can basically realize the real-time detection of traffic obstacles.

An ANN-based framework for estimating inconsistency in lateral placement of heterogeneous traffic

The lateral placement is the transverse position of a moving vehicle across the carriageway width. Studies on lateral placement have gained importance over time specifically for two reasons; (1) lateral placement data has become a crucial input in most of the traffic flow simulation models, and (2) detection of wheel positions helps in determining the riding quality and the distressed portions on a pavement surface. In the case of heterogeneous traffic flow and loosely enforced lane discipline, the estimation of lateral placement of vehicles deals with additional complexity. In such cases, vehicles take any lateral position left empty by other surrounding vehicles while moving in a mixed traffic stream. This results in an inconsistent lateral trajectory of vehicles. Further, this inconsistency is primarily governed by the subject vehicle type and other prevailing factors of the traffic stream. On this background, the present study forwards a Neural Network-based approach to quantify the inconsistency associated with the lateral placements chosen by different vehicle categories in a mixed traffic situation. In addition, a sensitivity analysis revealed how the inconsistency in lateral placement varies suggestively with the change in traffic and road geometric factors.

Courteous Behavior of Automated Vehicles at Unsignalized Intersections Via Reinforcement Learning

The transition from today's mostly human-driven traffic to a purely automated one will be a gradual evolution, with the effect that we will likely experience mixed traffic in the near future. Connected and automated vehicles can benefit human-driven ones and the whole traffic system in different ways, for example by improving collision avoidance and reducing traffic waves. Many studies have been carried out to improve intersection management, a significant bottleneck in traffic, with intelligent traffic signals or exclusively automated vehicles. However, the problem of how to improve mixed traffic at unsignalized intersections has received less attention. In this letter, we propose a novel approach to optimizing traffic flow at intersections in mixed traffic situations using deep reinforcement learning. Our reinforcement learning agent learns a policy for a centralized controller to let connected autonomous vehicles at unsignalized intersections give up their right of way and yield to other vehicles to optimize traffic flow. We implemented our approach and tested it in the traffic simulator SUMO based on simulated and real traffic data. The experimental evaluation demonstrates that our method significantly improves traffic flow through unsignalized intersections in mixed traffic settings and also provides better performance in a wide range of traffic situations compared to the state of the art.

The impact of a dedicated lane for connected and automated vehicles on the behaviour of drivers of manual vehicles

Connected and automated vehicles (CAVs) are expected to enhance traffic efficiency by driving at shorter time headways, and traffic safety by shorter reaction times. However, one of the main concerns regarding their deployment is the mixed traffic situation, in which CAVs and manually driven vehicles (MVs) share the same road.This study investigates the behavioural adaptation of MV drivers in car-following and lane changing behaviour when they drive next to a dedicated lane (DL) for CAVs and compares that to a mixed traffic situation. The expectation is that in a mixed traffic situation, the behavioural adaptation of MV drivers is negligible due to lower exposure time and scarce platoons, while concentrating the CAVs on one dedicated lane may cause significant behavioural adaptation of MV drivers due to a higher exposure time and conspicuity of CAV platoons.Fifty-one participants were asked to drive an MV on a 3-lane motorway in three different traffic scenarios, in a fixed-base driving simulator: (1) Base, only MVs were present in traffic, (2) Mixed, platoons of 2–3 CAVs driving on any lane and mixed with MVs, (3) DL, platoons of 2–3 CAVs driving only on a DL. The DL was recognizable by road signs and a buffer demarcation which separated the DL from the other lanes. A moderate penetration rate of 43% was assumed for CAVs.During the drives, the car following headways and the accepted merging gaps by participants were collected and used for comparisons of driving behaviour in different scenarios.Based on the results, we conclude that there is no significant difference in the driving behaviour between Base and Mixed scenarios at tested penetration rate, confirming our research expectation. However, in DL scenario, MV drivers drove closer to their leaders specially when driving on the middle lane next to the platoons and accepted shorter gaps (up to 12.7% shorter at on-ramps) in lane changing manoeuvres. Dedicating a lane to CAVs increases the density of CAV platoons on one lane and consequently their conspicuity becomes higher. As a result, MV drivers are influenced by CAV platoons on a DL and imitate their behaviour.The literature suggests that dedicating a lane to CAVs improves the traffic efficiency by providing more possibilities for platooning. This study shows that implementing such a solution will affect the driving behaviour of human drivers. This should be taken into consideration when evaluating the impacts of dedicated lanes on traffic efficiency and traffic safety.

ANALYSIS OF DRIVER BEHAVIOR IN VEHICLE FOLLOWING AND LANE CHANGING PARAMETERS ON URBAN MID BLOCK SECTIONS

In mixed traffic situations, there is weak or no lane behavior of the driver much more complicated where vehicle and driver behavior show a huge difference between them. Road traffic driving behavior on urban midblock sections is one of the most complex phenomena to be examined particularly in heterogeneous traffic conditions. This is often attributed to the capacity of the road section and the traffic flow features at the macroscopic and microscopic level of a road section. Very few researchers have attempted to investigate these features in heterogeneous environments because of the lack of adequate information gathering methods and the amount of complexity involved. In this background, an access controlled mid block road section was selected for video data collection. The main objectives of this study include developing vehicular trajectory data and analyzing the lane changing and vehicle following behavior of driver on the mid block section considering the relative velocities and relative spacing between various types of vehicles under heterogeneous traffic conditions. The videos were collected from urban roadway in the Kurnool district of Andhra Pradesh. The length of the stretch is 120m and the width is 7.0 m. The data was extracted to know the variations in terms of longitudinal and lateral speeds, velocities, vehicle following and lane changing behavior of the drivers. The data extracted was smoothened by moving average method to minimize the human errors. Lateral amplitude of the vehicles of various types was analyzed. The study revealed that vehicles in the mixed stream, in general and in particular, Bikes and Autos particularly move substantially in the lateral direction.

Road Boundary-Enhanced Automatic Background Filtering for Roadside Lidar Sensors

The roadside-deployed lidar sensor provides a solution to obtain the real-time, high-resolution micro traffic data (HRMTD) of all road users in the mixed traffic situation (connected vehicles and unconnected vehicles both exist on the roads). Background filtering is a necessary and important step for the HRMTD collection to serve the connected vehicle. Without excluding the background points, the accuracy of Euclidean-based object clustering and tracking algorithms can be reduced. The widely used method-3D-density-statistic filtering (3D-DSF) for roadside lidar background filtering can effectively exclude the background points for free-flow conditions. However, the performance of 3D-DSF can be greatly influenced by congested traffic conditions. This article presents a revised 3D-DSF algorithm to automatically extract background points by involving the road-boundary information. This new method is named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">road-boundary-enhanced, 3D-density statistic filtering</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3D-DSFRB</i> ). This algorithm involves the boundary of the historical trajectories of road users as the region of interest (ROI) to enhance the accuracy of background filtering. A revised grid-based method was developed for road-boundary ID. The 3D-DSF was only applied for the area outside of the ROI. Within the ROI, only ground surface was excluded. Case studies were conducted to evaluate the effectiveness of the 3D-DSFRB algorithm. The results showed that the 3D-DSFRB can filter background points for both free-flow conditions and congested traffic conditions. The time cost of the 3D-DSFRB was also reduced compared to the 3D-DSF. Compared to the state of the art, the 3D-DSFRB improved the accuracy of background filtering.

Evaluation of driver behavior and safety at urban mid-block sections under mixed traffic conditions

Driver behavior on road sections is one of the most difficult phenomena to study, particularly in mixed traffic situations on urban roads, especially on the midblock sections. In this context, video data was collected from an access-controlled mid-block road sections. The main aims of this study are to establish vehicular trajectory data and to analyse driver lane changing and vehicle following behavior on the selected mid-block sections while taking into account relative velocities and relative distances between different types of vehicles. In traffic safety assessments, the time to collision is an effective time-based safety measure for detecting rear-ends collisions. Hence, the safe distance between successive vehicles was estimated. The study revealed that vehicles in the mixed stream, in general and in particular, Bikes and Autos particularly move substantially in the lateral direction. The safe distance between vehicles has been found to be dependent on traffic flow and roadway width.

Performance Analysis of Model Predictive Intersection Control for Autonomous Vehicles

The paper focuses on the control challenge of intersections related to the appearance of autonomous vehicles on the roads, which established mixed traffic situations with human-driven vehicles or scenarios with only autonomous vehicles. The goal of the research is to control autonomous vehicles by Model Predictive Control method to guarantee the collision-free passage at the intersection. Generally the outcome of a traffic situation can be varied by human-driven vehicles and fully automated vehicles. Therefore the results of the proposed coordination method used for a given intersection scenario is compared to solution of human-driven vehicles. For the comparison the simulation examples were made in VISSIM and CarSim simulation environments.

Estimating lateral placement and lane indiscipline of urban mixed traffic of a developing country: an ANN-assisted approach

Information on lateral placement and lane indiscipline is useful in simulation of a mixed traffic stream and in identifying the distressed portion of a pavement. Despite this utility, inadequate investigation has been made to estimate the lateral placement of vehicles under prevailing traffic conditions. In a typical mixed traffic situation, vehicles having different static and dynamic characteristics take any lateral gap across the carriageway left empty by other surrounding vehicles and move in an untidy manner. This leads to variation in lateral placement of vehicles, governed by the subject vehicle type. This paper explores the potential factors that influence lateral placement of vehicles and presents an artificial neural network (ANN)-based approach to quantify lateral placement and lane indiscipline in the context of undivided urban roads. Further, sensitivity analysis revealed how different traffic parameters like traffic volume, traffic composition, and directional split influence lateral placement and lane indiscipline within each vehicle category.

Mathematical Definitions of Scene and Scenario for Analysis of Automated Driving Systems in Mixed-Traffic Simulations

This paper introduces a unified mathematical definition for describing commonly used terms encountered in systematical analysis of automated driving systems in mixed-traffic simulations. The most significant contribution of this work is in translating the terms that are clarified previously in literature into a mathematical set and function based format. Our work can be seen as an incremental step towards further formalisation of Domain-Specific-Language (DSL) for scenario representation. We also extended the previous work in the literature to allow more complex scenarios by expanding the model-incompliant information using set-theory to represent the perception capacity of the road-user agents. With this dynamic perception definition, we also support interactive scenarios and are not limited to reactive and pre-defined agent behavior. Our main focus is to give a framework to represent realistic road-user behavior to be used in simulation or computational tool to examine interaction patterns in mixed-traffic conditions. We believe that, by formalising the verbose definitions and extending theprevious work in DSL, we can support automatic scenario generation and dynamic/evolving agent behavior models for simulating mixed traffic situations and scenarios. In addition, we can obtain scenarios that are realistic but also can represent rare-conditions that are difficult to extract from field-tests and real driving data repositories.

Experience and Acceptance of Autonomous Shuttles in the Brussels Capital Region

Current technological developments allow the testing of shared autonomous electric vehicles in real-life conditions. Consequently, we can evaluate how users react and if these developments might lead to more sustainable transport behaviour in the future. The purpose of this study was to capture public opinion regarding autonomous vehicles in terms of user experience and intended future use. Autonomous shuttles were operated in the Brussels Capital Region in a mixed traffic situation, allowing interaction with other road users. We compared the results of two pilots with different target groups in the same city. Public opinion was captured through an online survey after passengers had experienced a ride. Our results showed that more than 70% of the passengers expressed no concerns with regards to autonomous vehicles. The majority had a positive experience with the shuttle and evaluated the shuttle positively in terms of driving behaviour, entry and exit and comfort. The regression analysis indicated that enjoyment of the ride is an important factor that contributes to the intention to use autonomous shuttles in the future.