Multi-lane Roads Research Articles

Altruistic Control of Connected Automated Vehicles in Mixed-Autonomy Multi-Lane Highway Traffic

We consider the problem of altruistic control of connected automated vehicles (CAVs) on mixed-autonomy multi-lane highways to mitigate moving traffic jams resulting from car-following dynamics of human-driven vehicles (HDVs). In most of the existing studies on CAVs in multi-lane settings, vehicle controller design philosophy is based on a selfish driving strategy that exclusively addresses the ego vehicle objectives. To improve overall traffic smoothness, we propose an altruistic control strategy for CAVs that aims to maximize the driving comfort and traffic efficiency of both the ego vehicle and surrounding HDVs. We formulate the problem of altruistic control under a model predictive control (MPC) framework to optimize acceleration and lane change sequences of CAVs. In order to efficiently solve the resulting non-convex mixed-integer nonlinear programming (MINLP) problem, we decompose it into three non-convex subproblems, each of which can be transformed into a convex quadratic program via penalty based reformulation of the optimal velocity with relative velocity (OVRV) car-following model. Simulation results demonstrate significant improvements in traffic flow via altruistic CAV actions over selfish strategies on both single- and multi-lane roads.

Operational Evaluation of Climbing Lanes on Steep Grades and Heavy Trucks

Nearly 99% of Colombian roads have two lanes. In order to improve their level of service (LOS), they have been turned into multilane roads through the doubling of existing highways without technical criteria to justify such transformation, and without considering more appropriate alternatives such as auxiliary lanes. Due to Colombia’s mountainous topography and the high weight/power ratio of the vehicles in circulation, research on auxiliary lanes has been carried out to clarify their applicability. However, operational evaluation procedures such as the Highway Capacity Manual (HCM) may not be representative for the infrastructure and vehicles in Colombia.The hypothesis is that the operational benefits of climbing lanes on two-lane roads with Colombian infrastructures and trucks are higher than the HCM estimate. The objective is to carry out an operational evaluation of the effect of auxiliary lanes on traffic in 84.000 scenarios, and to determine threshold values of operational indicators in order to provide guidance for a better design of this kind of infrastructure.The operational analysis was performed on a virtual road with an auxiliary lane with Colombian trucks and a variety of road geometry and traffic features. Traffic operations were simulated with TWOPAS, part of the Interactive Highway Safety Design Model (IHSDM), for which the computational program PEMAT was created. The results demonstrate the benefits of auxiliary lanes on the improvement of Percent Time Spent Following (PTSF) and Average Travel Speed (ATS), and define criteria for geometric design.

Usage of Microscopic Simulation to Estimate the Environmental Impact of Road Transport

The purpose of the transport system is providing access to goods, jobs and services. However, the increase in the level of transport services provision is accompanied by negative consequences. Within the concept of sustainable development of urban transport systems, it is necessary to ensure a safe environmental load for people. Therefore, the purpose of this study is the search and testing of methods to correctly estimate the degree of environmental impact by road transport, reducing the environmental burden, and substantiating the rationality of the implementation of the proposed measures for greening transport. As research methods were used: the theory of traffic flows, simulation, computer experiment, methods of direct measurement of environmental emissions on the road segment under study. The use of simulation allows to find an acceptable solution for the volume of exhaust gases and noise pollution, by varying the parameters of the transport system objects (the configuration of the road network, the characteristics and modes of operation of the road infrastructure elements). It is shown that micromodelling is an effective approach, in which the unit of modeling is a separate vehicle. It makes possible to take into account the individual characteristics of the vehicle (current mileage, engine type, environmental safety class), which allows an estimation of the environmental impact of the vehicle fleet operating in the area. Examples of using this approach are given, computer experiments on specific sections of the road network are conducted (in particular at intersections of multi-lane roads with traffic lights, as well as locations of road service facilities, such as gas stations, car parks, etc., which increase the negative environmental impact on the adjacent territory).

Study of Freezing Scheme in Artificial Horizontal Ground Freezing Method in Multi-lane Road Tunnel Based on Numerrical Simulation of Temperature Field

Artificial ground freezing method to form a frozen soil wall to cut off water and resist the soil pressure before excavation, was rarely used in Bangladesh such a vast hilly area. In this paper, connecting channel construction in Multi-lane Road Tunnel under the River Karnaphuli Chittagong – Bangladesh which is proposed to be constructed by double-row-ring freezing method, is taken as the study object. Taking the development of temperature field, the resisting ability of frost wall to thermal disturbance resulted from excavation, analysis of different freezing condition by numerical simulation is carried out for active and maintaining freezing stages, so that some guidance could be given for the final selection of freezing scheme. The results show that the design and construction scheme were reasonably practicable.

Empirical study on capacity evaluation of urban multi-lane roads under heterogeneous traffic conditions

The capacity of a highway facility is defined as the maximum hourly flow rate at which persons or vehicles can reasonably be expected to traverse a point or uniform section of a lane or a roadway during a given time period under prevailing roadway and traffic conditions (HCM, 2010) (Transport Research Board 2010). Even though the definition of capacity is universal capacity is a value dependent upon various traffic and roadway characteristics. Hence different countries have developed indigenous guidelines to estimate the traffic carrying capacity of their roads. This study is a steppingstone to the development of such a guideline for Sri Lanka. The study looks into the applicability of the HCM 2010 guideline for local conditions and observes the empirical evidence. It is found that the HCM guideline is not applicable to local conditions given the low traffic stream speeds. Further, capacity values of 25 four-lane highway sections are estimated using first principles assuming Greenshields’ model. Capacity values ranging from 2399 pcu/h/l to 1346 pcu/h/l were observed. Even though they are comparable with capacity values observed in homogeneous traffic streams in developed countries the speeds at capacity were found to be drastically low.

Influence of Safety in Performance Assessment of Two-Lane Highways: A Critical Review

Traffic operations on two-lane roads are different from that of multilane roads because of the presence of opposing traffic. More often than not, faster vehicles get impedance from slower ones, entrapped inside platoons and delayed. The delay caused to these vehicles depends on both the flow of traffic and the proportion of slower vehicles in its composition. Drivers’ of delayed vehicles search for an acceptable gap to perform passing operations. At times, a part of drivers becomes reluctant to move in platoons, especially when such delay is long enough, and they start exhibiting risk-taking behaviour while passing impeding slower vehicles. Such attempts significantly affect operations and safety of traffic. The problem is, however, more acute when the prevailing traffic is heterogeneous with a sizable proportion of slower vehicles. The present study, thus, elucidates the need for considering both operation and safety in the performance assessment of two-lane two-way highways under such traffic situations.

Una revisión de modelos de tráfico automotor usando autómatas celulares Luis Bladismir Ruiz

Los Autómatas Celulares son un modelo matemático utilizado para estudiar y representar sistemas dinámicos, adecuados para modelar sistemas naturales como la evolución de virus y bacterias, así como el flujo de gases, líquidos y del tráfico vehicular y peatonal. El primer modelo probabilístico no trivial fue presentado por Nagel y Schreckenberg (1992) y cada vez son más los investigadores que se suman a su aplicación en la vida diaria. En este artículo se plantea una revisión del comportamiento del flujo vehicular (FV) para vías de uno y varios carriles, considerando las modificaciones de los modelos ya existentes y los cambios en la forma de conducir con la finalidad de conocer el enfoque de los diversos investigadores sobre el estudio de tráfico vehicular. El estudio revela que el FV se adapta a las condiciones del tráfico del momento y que el modelo, por su propiedad, permite modificar reglas de interacción y considerar la infraestructura de la vía para el modelo de un carril mientras que los modelos con carriles múltiples son más complejos.

Modeling pedestrian gap acceptance behavior at a six-lane urban road

The illegal crossing behavior of pedestrians at unmarked mid-block sections is unsafe, risky, and increase the potential of severe conflicts and crashes, especially in the case of multilane roads. This paper investigates the pedestrians’ gap acceptance while crossing the road illegally at an unmarked mid-block segment of a six-lane divided urban road. A multiple regression model was developed to estimate the size of the accepted gaps as a function of the demographics, crossing behavior, and traffic-related characteristics. The model suggested that waiting time, crossing point, rolling gap, vehicle speed, critical distance, and vehicle position have a strong influence on the size of the gap. The findings of this study can be useful for simulation modeling, enforcement efforts, and education and training programs.

Investigation of pedestrian crashes using multiple correspondence analysis in India

Pedestrian safety is of growing concern with an increasing number of traffic accidents, especially in developing economies like India. In 2017, there were 20,457 pedestrian fatalities in India. Pedestrian crashes have also become a key concern in the state of Tamilnadu, India, due to the high percentage of deaths. If the available datasets are large and complex, identifying key factors is a challenging task. In this study, Multiple Correspondence Analysis (MCA), an exploratory data analysis technique was used to explore the roadway, traffic, crash, and pedestrian-related variables influencing pedestrian crashes. This study used the data from Government of Tamilnadu Road Accident Traffic Management System (RADMS) database, to analyse accident data of nine years (2009–2017) related to pedestrian crashes. The results of the study show that crashes occurring on the express highways on a multilane road are often associated with hit-and-run behaviour among drivers. Factors such as lighting conditions, location, pedestrian behaviour, crossings, and physical separation are also significantly contributing to pedestrian crashes. The key advantage of MCA is that it identifies a possible association between various contributing factors. The findings from this study will be useful for state transport authorities to improve countermeasures for mitigating pedestrian crashes and fatalities.

A Case Study on the Impact of Crosswalk Markings on Driver Yielding to Pedestrians

The research team measured yielding at marked, unsignalized crosswalks as part of a multifaceted high visibility enforcement intervention intended to improve pedestrian safety. One of the crosswalks had its markings removed. With concern to possible reduced driver expectancy for pedestrians after the removal of the crosswalk, measurement continued at that location as a case study on the impact of crosswalks on drive yielding on multilane roads. Measurement of yielding at the unmarked crosswalk, when compared to similarly marked crosswalks, observed less yielding overall and more yielding at closer distances to the unmarked crosswalk, which poses a significant risk to pedestrians. The findings have implications for traffic infrastructure planners attempting to optimize safety for pedestrians.

An extension of Newell’s simplified kinematic wave model to account for first-in-first-out violation: With an application to vehicle trajectory estimation

Newell’s simplified kinematic wave model has been widely used in studying traffic dynamics in a road network. However, it cannot account for First-In-First-Out violation among vehicles on multi-lane roads. In this study, we present an extension of the model in the Lagrangian coordinates. In particular, we define a new variable of a vehicle’s order as its corresponding cumulative flows at different times and locations. The new kinematic wave model is consistent at the aggregate level with Newell’s simplified kinematic wave model, but allows for FIFO violation among individual vehicles.Based on the new model, we then present two algorithms to estimate vehicles’ trajectories from Eulerian data (cumulative flows provided by loop detectors) and Lagrangian data (the vehicle’s entry and exit times provided by reidentification technologies) at two boundaries of a road segment. In the first algorithm, we assume that vehicles follow the First-In-First-Out (FIFO) principle on a multi-lane road, and their orders are constant and equal the average of the entry and exit orders; in the second algorithm, we introduce a linear order-changing model to interpolate a vehicle’s orders at different times according to its entry and exit times. With Next Generation Simulation (NGSIM) datasets, we demonstrate that both algorithms are effective, but the second one, with an estimation error of around 10%, greatly outperforms the first one, with an estimation error of around 13%. This verifies the advantage of incorporating FIFO violation in the new model.

The Impact of Pedestrian Crossing Flags on Driver Yielding Behavior in Las Vegas, NV

Walking is the most affordable, accessible, and environmentally friendly method of transportation. However, the risk of pedestrian injury or death from motor vehicle crashes is significant, particularly in sprawling metropolitan areas. The purpose of this study was to examine the effect of pedestrian crossing flags (PCFs) on driver yielding behaviors. Participants crossed a marked, midblock crosswalk on a multilane road in Las Vegas, Nevada, with and without PCFs, to determine if there were differences in driver yielding behaviors (n = 160 crossings). Trained observers recorded (1) the number of vehicles that passed in the nearest lane without yielding while the pedestrian waited at the curb and (2) the number of vehicles that passed through the crosswalk while the pedestrian was in the same half of the roadway. ANOVA revealed that drivers were significantly less likely to pass through the crosswalk with the pedestrian in the roadway when they were carrying a PCF (M = 0.20; M = 0.06); drivers were more likely to yield to the pedestrian waiting to enter the roadway when they were carrying a PCF (M = 1.38; M = 0.95). Pedestrian crossing flags are a low-tech, low-cost intervention that may improve pedestrian safety at marked mid-block crosswalks. Future research should examine driver fade-out effects and more advanced pedestrian safety alternatives.

A formulation of unifiable multi-commodity kinematic wave model with relative speed ratios

Observations suggest that the First-In-First-Out (FIFO) principle is usually violated on multi-lane roads. Jin (2017) formulated and solved unifiable multi-commodity kinematic wave models, when different commodities have the same contributions to overall traffic congestion (unifiable) but may travel at different speeds (non-FIFO). However, the construction of unifiable multi-commodity fundamental diagrams and the assumption of concave or convex commodity flow proportion functions are purely mathematical and lack behavioral explanations. Thus the existing formulation is only of pure mathematical and theoretical interests and too complicated for real-world calibration, validation, or applications.In this study, we present a new formulation of unifiable multi-commodity kinematic wave models to address the aforementioned limitations. We first introduce a new variable for the relative speed ratios of different commodities and particularly discuss constant relative speed ratios. The relative speed ratios are physically, behaviorally, and economically meaningful since they characterize drivers’ relative aggressiveness, values of times, and other features. We then present unifiable multi-commodity fundamental diagrams based on the relative speed ratios, which can be used to derive the mathematical generating functions in Jin (2017). Then we show that non-FIFO multi-commodity kinematic wave model is a system of non-strictly hyperbolic conservation laws and solve the Riemann problem for a two-commodity system with constant relative speed ratios, in which the commodity flow proportion function is either concave or convex. We also present an empirical evidence for the existence of unifiable multi-commodity fundamental diagrams with constant relative speed ratios, which help to demonstrate the advantage of the new formulation. In summary, the new formulation is as general as the original formulation, but physically more meaningful, theoretically easier to solve, and empirically simpler to calibrate. Finally we conclude the study with discussions on potential future applications.

Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways

The current paper proposes a novel stochastic procedure for modelling car-following behaviours on a multi-lane motorway. We develop an integrated multi-lane stochastic continuous car-following model where a deep learning architecture is used to estimate a probability of lane-changing (LC) manoeuvres. To the best of our knowledge, this work is among the very few papers which exploit deep learning to model driving behaviour on a multi-lane road. The objective of this study is to establish a coupled stochastic continuous multi-lane car-following model using Langevin equations to cope with probabilistic characteristics of LC manoeuvres. In particular, a stochastic volatility, derived from LC manoeuvres is introduced in a multi-lane stochastic optimal velocity model (SOVM). In additions, Convolutional Neural Network (CNN) is applied to estimate a probability of LC manoeuvres in the integrated multi-lane car-following model. Furthermore, imaged second-based trajectories of the lane-changer and surrounding vehicles are used to identify whether LC manoeuvres occur by using the CNN. Finally, the proposed method is validated using a real-world high-resolution vehicle trajectory dataset. The results indicate that the prediction of the integrated SOVM is almost identical to the observed trajectories of the lane-changers and the following vehicles in the initial and the target lane. It has been found that the proposed multi-lane SOVM can tackle the unpredictable fluctuations in the velocity of the vehicles in the acceleration/deceleration zone.

Cyberphysical risks of hacked internet-connected vehicles.

The integration of automotive technology with internet connectivity promises to both dramatically improve transportation while simultaneously introducing the potential for new unknown risks. Internet-connected vehicles are like digital data because they can be targeted for malicious hacking. Unlike digital data, however, internet-connected vehicles are cyberphysical systems that physically interact with each other and their environment. As such, the extension of cybersecurity concerns into the cyberphysical domain introduces new possibilities for self-organized phenomena in traffic flow. Here we study a scenario envisioned by cybersecurity experts leading to a large number of internet-connected vehicles being suddenly and simultaneously disabled. We investigate posthack traffic using agent-based simulations and discover the critical relevance of percolation for probabilistically predicting the outcomes on a multilane road in the immediate aftermath of a vehicle-targeted cyberattack. We develop an analytic percolation-based model to rapidly assess road conditions given the density of disabled vehicles and apply it to study the street network of Manhattan (New York City, New York, USA) revealing the city's vulnerability to this particular cyberphysical attack. While a comprehensive investigation of city-scale traffic around hacked vehicles is an extremely complicated problem, we find that the statistical physics of percolation can provide an estimate of the number of vehicles that critically disrupts citywide traffic flow. Our upper-bound estimate represents a quantification of citywide traffic disruptions when multiple vehicles are hacked.

Multi-class traffic flow on a partially space-shared road

ABSTRACTScarce urban space makes urban streets have to be partly shared by cars and cyclists. Traffic flow can be considerably influenced by cyclists, which are comparatively slower than cars. The fundamental diagram (FD) can change greatly, correlating to bike lane length and the cyclist flow. Models for the traffic dynamics on the space-shared streets are still lacking. This work fills the gap. This paper starts from proposing analytic formulas for the capacity, free-flow speed and critical density on a one-lane road, which together characterize the FD. The sensitivity of the FD to the bike lane length as well as the cyclist flow, using the analytical formulas, are presented. The given FD is satisfactory, compared with microscopic simulation results. Finally, we extend the research scope to a multi-lane road. It is expected that this work can benefit city designs and traffic operations.

A mathematical model of traffic segregation on multilane road

We study a process of segregation of traffic flow on a multilane highway. Our study is based on the stochastic mathematical model developed by Buslaev A.P. et al. in 2006, 2008 years. This model is based on the deterministic-stochastic approach that was developed by Buslaev in 2005. In the model, particles, moving on a lattice, correspond to vehicles. We consider the problem of optimization of segregation characteristics. We consider an example of segregation process on five-lane segment of a road. The length of the segment equals 4000 meters. The calibration of the model parameters is based on empirical data obtained in measurements on roads.

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.