Multi-lane Roads Research Articles

Complex urban dataset with multi-level sensors from highly diverse urban environments

The high diversity of urban environments, at both the inter and intra levels, poses challenges for robotics research. Such challenges include discrepancies in urban features between cities and the deterioration of sensor measurements within a city. With such diversity in consideration, this paper aims to provide Light Detection and Ranging (LiDAR) and image data acquired in complex urban environments. In contrast to existing datasets, the presented dataset encapsulates various complex urban features and addresses the major issues of complex urban areas, such as unreliable and sporadic Global Positioning System (GPS) data, multi-lane roads, complex building structures, and the abundance of highly dynamic objects. This paper provides two types of LiDAR sensor data (2D and 3D) as well as navigation sensor data with commercial-level accuracy and high-level accuracy. In addition, two levels of sensor data are provided for the purpose of assisting in the complete validation of algorithms using consumer-grade sensors. A forward-facing stereo camera was utilized to capture visual images of the environment and the position information of the vehicle that was estimated through simultaneous localization mapping (SLAM) are offered as a baseline. This paper presents 3D map data generated by the SLAM algorithm in the LASer (LAS) format for a wide array of research purposes, and a file player and a data viewer have been made available via the Github webpage to allow researchers to conveniently utilize the data in a Robot Operating System (ROS) environment. The provided file player is capable of sequentially publishing large quantities of data, similar to the rosbag player. The dataset in its entirety can be found at http://irap.kaist.ac.kr/dataset.

Use of Open Data to Assess Cyclist Safety in London

This study develops a predictive model for cycling collisions in London. Specifically, the effects of bus lanes, parking or loading facilities, and multilane roads on the risk of cycling collisions are considered. To the best of the authors’ knowledge, this is the first such predictive collision model that develops covariates to measure the characteristics of different types of road infrastructure within zones. A kernel density estimator is used to identify 90 collision hotspots. Each hotspot is populated with information regarding the highway infrastructure within it. A multiple linear regression model tests for the statistical significance of the infrastructure variables. Bus lanes, multilane roads, and 30-mph speed limits are found to affect cycle collision counts, whereas junction density has the largest impact on collision density. Speed limits of 20 mph affect collision counts to a lesser degree than 30 mph, indicating potential safety improvement from reducing speed limits. One-way roads are found to reduce the risk of collisions, along with the provision of priority junctions. This infers that other junction types, such as roundabouts and signalized junctions, present a higher collision risk. The models produce conflicting results on parking or loading provision. The models are expanded to include sociodemographic variables, such as population and employment. The combined model offers no performance improvement over the infrastructure-only model, although a potential link between public transport provision and reducing cycle collisions warrants further investigation.

Multi-vehicle tracking with microscopic traffic flow model-based particle filtering

This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks.

Behavior Monitoring Using Learning Techniques and Regular-Expressions-Based Pattern Matching

This paper addresses the problem of maneuver recognition and behavior anomaly detection for generic targets by means of pattern matching techniques. The problem analysis is performed making specific reference to moving vehicles in a multi-lane road scenario, but the proposed technique can be easily extended to significantly different monitoring contexts. The potential extensions include, but are not limited to, public surveillance in train station or airport, road incidents and relative precursors detection, and vehicle trajectories monitoring. The overall proposed solution consists of a trajectory analysis tool and a string-matching method. This allows the integration of two different approaches, to detect both a priori defined patterns of interest and generic maneuver/behavior standing out from those regularly exhibited. The proposed string matching algorithm is newly developed in this paper, based on Regular Expressions. For generating reference patterns, a technique for the automatic definition of a dictionary of regular expressions matching the commonly observed target maneuvers is developed. The advantages of the proposed approach are extensively analyzed and tested by means of numerical simulations and experiments.

Simulation of Road Capacity Considering the Influence of Buses

The traditional cellular automata model cannot fully demonstrate the effect of buses on road capacity due to the incapable of considering the difference in dynamic performance and driving behaviors between buses and other vehicles. In this paper, a novel cellular automata model accounting for car-following and lane-changing behaviors was developed to investigate the relationship between the multilane road capacity and the proportion of buses. The parameters of the model were calibrated and verified using the data collected from a real-word road with heterogeneous traffic flow. The calibration result indicates that the proposed model can accurately describe the evolution of traffic dynamics on multilane roads. It is found that drivers have a specific preference in determining the target lane in the lane-changing process. Under the same conditions, bus drivers prefer to choose the side lane of the road, while drivers of other vehicles prefer to select the middle lanes. Besides, the preference for the target lanes affects the lane-changing behaviors of the drivers, resulting in a considerable difference in optimal traffic density and maximum traffic volume in different lanes. Moreover, the simulation result suggests that the proportion of buses has a significant impact on road capacity (e.g., the capacity is reduced by as much as 18%). To increase road capacity and improve bus services, based on the proposed model, transit authorities can dynamically allocate the limited road space to buses and other vehicles according to the proportion of buses.

Comprehensive Predictive Control Method for Automated Vehicles With Delays

This paper considers the control problem of automated vehicles with delays. We develop a comprehensive control method within the framework of model predictive control (MPC) with compensation for the input delay and the state delay for automated vehicles on the multi-lane road, where the road boundaries and lane markings are modeled as potential functions. The original vehicle dynamics model is modified to formulate the augmented prediction model to compensate for delays. We also present a parallel strategy for the executions of the perception system, the controller, and the actuator, which can directly cut delays in the loop. The simulations show that this method can greatly reduce the impact of delays on the performance of the controller and, therefore, enhance its capabilities.

The effect of motorcycle proportion on traffic flow parameters at multi-lane urban road

The phenomenon of traffic flows in almost all major cities of Indonesia is large dominance in the use of motorcycles. The change in the proportion of motorcycle users on the streets reaches more than 50%. The author will discuss the effect of motorcycle volume proportion on traffic flow parameters such as speed and density. This research has been conducted at Ampang street with 4 lanes 2-way undivided type and Adinegoro street with 4 lanes 2-way divided type in Padang. By using Video Image Processor survey method, the data of traffic volume, and time travel of the vehicle for 12 hours were obtained. Further data is processed and the results are plotted into the graph by linear regression method. The results showed that the highest proportion of motorcycle volume of 75.9% occurred on Raya Ampang street and the lowest by 53.2% occurred on Adinegoro street (to the North). The result of linear regression analysis where proportion of motorcycle volume (p) as variable x and space-mean-speed (S) as variable y is S = -0.687(p) + 93.467, R2 = 0.8582 at Ampang street, S = -1.3273(p) + 143.75, R2 = 0.8153 at Adinegoro street (to the south), and y = -1.3861(p) + 131.94, R2 = 0.7882 at Adinegoro street (to the north). Then the relationship between space-mean-speed, density, and traffic volume analyzed.

A MultiLane Macroscopic Traffic Flow Model for Simple Networks

We prove the well-posedness of a system of balance laws inspired by [H. Holden and N. H. Risebro, SIAM J. Math. Anal., 51 (2019), pp. 3694--3713], describing macroscopically the traffic flow on a multilane road network. Motivated by real applications, we allow us for the the presence of space discontinuities both in the speed law and in the number of lanes. This allows us to describe a number of realistic situations. Existence of solutions follows from compactness results on a sequence of Godunov's approximations, while $\mathbf{L^1}$-stability is obtained by the doubling of variables technique. Some numerical simulations illustrate the behavior of solutions in sample cases.

A portable roadside vehicle detection system based on multi-sensing fusion

To improve traffic efficiency, the intelligent transportation system (ITS) is widely used in urban roads. The sensing system is preferred to be deployed at roadside because the normal traffic would not be interfered. In the field of the magnetic vehicle detection, one of the most challenging problems is the disturbance from the other lane. Most existing magnetic detection systems need more than one magnetic sensor to solve the disturbance. This paper proposes a portable roadside vehicle detection system based on multi-sensing fusion. Cooperated with the magnetic sensor and the ultrasonic sensor, the disturbance from non-detected lane can be excluded and the traffic volume of the detected lane can be calculated accurately. Through on-road experiments, the vehicle detection accuracy can achieve 97.14% in a two-lane road. The experimental results demonstrate that the sensing system is able to be deployed at roadside in multi-lane roads to get traffic flow reliably.

Examining the Impact of Land-use Related Factors on Rural Traffic Collisions

Background: More than 70% of fatal collisions belong to rural roads in Iran. A large number of these rural roads are no access control that allows the direct connection of land-uses to the main road. Direct connection of land-uses to the main road introduces conflicts and friction into the traffic stream that increases collision potential. This paper explores the Impact of area-based land-use variables on the frequency of traffic collisions on a divided rural multilane road. Methods: A case study of Rasht-Anzali road is chosen for this study. The collision data is obtained from the highway police department of Guilan province containing four years of collisions occurred in this road from 2014 to 2017. The land-use data is obtained from the rural guide plan of the villages locating beside the roadway supplemented by Google Earth maps. To develop the model inputs, the road centerline is buffered in both sides, and then the buffered roadway is divided into equal segments in each way due to the median dividing of the highway. The collision data has been divided into three different groups of collisions: vehicle-to-vehicle, pedestrian involved and all collisions. Count regressions are used to predict the frequency of collisions in road segments using land-use variables. With attention to over dispersion and excess zeros on the dependent variable, negative binomial and zero-inflated negative binomial regressions are used in this study. The best model for each group is chosen based on Vuong goodness-of-fit measure. Results: The results confirm some land-use impacts in previous researches and suggest some further implications for the interaction between land-uses and rural traffic collisions. With referring to models estimations, commercial, residential, government, and institutional land-uses played an important role in both vehicle-to-vehicle and pedestrian collisions, and religious land-use is found to have an increasing impact on pedestrian collisions. Conclusion: The findings can be useful in understanding the complex interrelationships between land-use and rural collisions, and in facilitating planners and policymakers to build safer roads.

Multilane roads extracted from the OpenStreetMap urban road network using random forests

AbstractThe volunteered geographic information (VGI) collected in OpenStreetMap (OSM) has been used in many applications. Extracting multilane roads and establishing a high level of expressed detail play important roles in the field of automated cartographic generalization. An accurate and detailed extraction process benefits geographic analysis, urban region division, and road network construction, as well as transportation applications services. The road networks in OSM have a high level of detail and complex structures; however, they also include many duplicate lines, which degrade the efficiency and increase the difficulty of extracting multilane roads. To resolve these problems, this work proposes a machine‐learning‐based approach, in which the road networks are first converted from lines to polygons. Then, various geometric descriptors, including compactness, width, circularity, area, perimeter, complexity, parallelism, shape descriptor, and width‐to‐length ratio, are used to train a random forest (RF) classifier and identify the candidates. Finally, another RF is trained to evaluate the candidates using all the geometric descriptors and topological features; the outputs of this second trained RF are the predicted multilane roads. An experiment using OSM data from Beijing, China validated the proposed method, which achieves a highly effective performance when extracting multilane roads from OSM.

Infrastructure‐based vehicular congestion detection scheme for V2I

SummaryWith the increasing number of vehicles, traffic jam becomes one of the major problems of the fast‐growing world. Intelligent transportation system (ITS) communicates perilous warnings and information on forthcoming traffic jams to all vehicles within its coverage region. Real‐time traffic information is the prerequisite for ITS applications development. In this paper, on the basis of the vehicle‐to‐infrastructure (V2I) communication, a novel infrastructure‐based vehicular congestion detection (IVCD) scheme is proposed to support vehicular congestion detection and speed estimation. The proposed IVCD derives the safety time (time headway) between vehicles by using iterative content‐oriented communication (COC) contents. Meanwhile, the roadside sensor (RSS) provides an infrastructure framework to integrate macroscopic traffic properties into the estimation of both the traffic congestion and vehicle safety speed. The main responsibilities of RSS in IVCD are to preserve privacy, aggregate data, store information, broadcast routing table, estimate safety speed, detect traffic jam, and generate session ID (S‐ID) for vehicles. Monte Carlo simulations in four typical Chinese highway settings are presented to show the advantage of the proposed IVCD scheme over the existing Greenshield's and Greenberg's macroscopic congestion detection schemes in terms of the realized congestion detection performance. Real road traces generated by Simulation of Urban Mobility (SUMO) over NS‐3.29 are utilized to demonstrate that the proposed IVCD scheme is capable of effectively controlling congestion in both single and multilane roads in terms of density and speed health with previous schemes in this field.

Robust Drivable Road Region Detection for Fixed-Route Autonomous Vehicles Using Map-Fusion Images.

Environment perception is one of the major issues in autonomous driving systems. In particular, effective and robust drivable road region detection still remains a challenge to be addressed for autonomous vehicles in multi-lane roads, intersections and unstructured road environments. In this paper, a computer vision and neural networks-based drivable road region detection approach is proposed for fixed-route autonomous vehicles (e.g., shuttles, buses and other vehicles operating on fixed routes), using a vehicle-mounted camera, route map and real-time vehicle location. The key idea of the proposed approach is to fuse an image with its corresponding local route map to obtain the map-fusion image (MFI) where the information of the image and route map act as complementary to each other. The information of the image can be utilized in road regions with rich features, while local route map acts as critical heuristics that enable robust drivable road region detection in areas without clear lane marking or borders. A neural network model constructed upon the Convolutional Neural Networks (CNNs), namely FCN-VGG16, is utilized to extract the drivable road region from the fused MFI. The proposed approach is validated using real-world driving scenario videos captured by an industrial camera mounted on a testing vehicle. Experiments demonstrate that the proposed approach outperforms the conventional approach which uses non-fused images in terms of detection accuracy and robustness, and it achieves desirable robustness against undesirable illumination conditions and pavement appearance, as well as projection and map-fusion errors.

Impact assessment of driver speed limit compliance behavior on macroscopic traffic characteristics under heterogeneous traffic environment

ABSTRACTThis study aims to evaluate the change in roadway capacity with increasing driver compliance levels for a given posted speed limit. The simulation experiment was conducted for all-cars traffic and varying traffic compositions on an eight-lane divided multi-lane urban road in Delhi, India. It is found that with increasing driver compliance level for a given posted speed limit, there is an increase in roadway capacity but with marginal increase in the critical speed of the traffic stream. Also, higher critical speeds are observed at very high and very low compliance levels. Furthermore, based on speed–flow plot developed from field observed conditions, the present compliance on selected multilane urban road at stream level is found as 40% approximately. The results of the study may be useful in underlining the importance of driver compliance with posted speed limit along with its enforcement for better safety.

Organization of traffic flows’ simulation aimed at establishment of integral characteristics of their dynamics

The paper presents an approach aimed at the study of various aspects of traffic passage through a multi-lane road that is based on computational experiments. Microscopic simulation based on models of the type “leader-following models” is considered to be the most adequate and the most accurate means for performing them. The leader-following model of the entire traffic flows in a city road network acquires the more holistic form via the formalism of hybrid dynamic systems or, in other words, event-switched systems. The general formalization of this approach is presented and the corresponding representation of multi-lane traffic according to this approach is presented. The latter includes the detailed formal description of the traffic flow carrier with a certain traffic organization as well as the description of conditions for drivers’ choice of acceleration/braking and lane change according to incentive motives for it and positions and speeds of neighboring vehicles and the vehicle itself. Organization of computational experiments allowing to establish the dependence of the average speed of traffic on the density of incoming flows, the distribution of various types of vehicles and their drivers, road organization and other factors in a multi-lane road is considered. It is demonstrated in what way they allow to evaluate quantitatively the dependence of the road throughput on the above factors. Results of calculations are presented and analyzed.

Validating Area Occupancy–Based Passenger Car Units and Homogeneous Equivalent Concept under Mixed Traffic Conditions in India

AbstractThis research work puts an emphasis on passenger car unit (PCU) estimation using different methods on eight-lane divided urban multilane roads using a well-calibrated and validated microsim...

Influence of Advanced Placement of the In-Street Sign Gateway on Distance of Yielding from the Crosswalk

A multiple-threat accident occurs when a pedestrian attempts to traverse a crosswalk on a multilane road, and the vehicle nearest to the pedestrian yields close to the crosswalk, obscuring the pedestrian’s view of the next lane, and the motorist’s view in the adjacent lane of the pedestrian. This set of circumstances may lead to pedestrians being struck at higher speeds by a vehicle in the adjacent oncoming lane, often resulting in serious injury or a fatality. The present study compared advanced placement of a gateway in-street sign configuration with a gateway deployed at the crosswalk, on two multilane roads. Data were collected using a reversal design, capturing alternating series of data points in each condition, and counterbalancing the order of those conditions. The advanced placement of the sign produced comparable levels of yielding to the crosswalk placement, but produced a large increase in the proportion of yielding 50 ft or further from the crosswalk. On multilane roads, increasing the distance at which vehicles yield from the crosswalk mitigates the sight distance issues that produce multiple-threat accidents, thereby reducing the likelihood of pedestrian fatalities or serious injuries. Advanced placement of the gateway configuration may also take the signs out of the turning radii of larger vehicles at a driveway or intersection, which may improve the longevity of the in-street signs.

A study of at-fault older drivers in light-vehicle crashes in Singapore

A number of studies on motor vehicle crashes have suggested that older drivers are more likely to be at-fault compared to younger drivers. The objective of this paper is to identify factors that contribute to older drivers (aged 65 and above) being at fault in light vehicle crashes in Singapore. Based on 3 years of crash data, the calibrated binary logit model shows that older drivers are more likely to be at fault during peak periods and festive seasons between November to February, as well as at gore areas of expressways, intersections. Curb lanes of multi-lane roads and single-lane roads are also found to increase the odds of older drivers being at fault. Furthermore, older drivers appear to have more problems on roads with wet surfaces and speed limits of 60 km/h and 70 km/h. In the light of an aging population in Singapore, it is imperative that more targeted countermeasures be taken from multiple perspectives to lower such risks.

A Mean Field Game approach for multi-lane traffic management

Abstract In this work, we discuss a Mean Field Game approach to traffic management on multi-lane roads. The control is related to the optimal choice to change lane to reach a desired configuration of the system. Such approach is particularly indicated to model self-driven vehicles with complete information on the state of the system. The mathematical interest of the problem is that the system of partial differential equations obtained is not in the classic form, but it consists of some continuity equations (one for each lane) and a variational inequality, coming from the Hamilton-Jacobi theory of the hybrid control. We propose a consistent semi-Lagrangian scheme for the approximation of the system and we discuss how to improve its efficiency with the use of a policy iteration technique. We finally present a numerical test which shows the potential of our approach.

Study on Single Camera Based ANPR System for Improvement of Vehicle Number Plate Recognition on Multi-lane Roads

In this paper, we introduce the single camera-based number recognition system used for this system recognizes vehicle number plates on one lane by using a single camera. Intelligent transport system (ITS) has been constructed because there is a limit in solving traffic problems in a physical manner such as construction of roads and subways. The single camera-based number recognition system used for this system recognizes vehicle number plates on one lane by using a single camera. Due to the increased cost of the installation and maintenance thereof, there is a growing need for a multi-lane-based number recognition system. When the single camera-based number recognition system is used for multi-lane recognition, the recognition rate is lowered due to a difference in vehicle image size among lanes and a low-resolution problem. Therefore, in this study, we applied a character extraction algorithm using connected vertical and horizontal edge segments-based labeling to improve multi-lane vehicle number recognition rate and thereby to allow application of the single camera-based system to multi-lane roads.