Multiple-lane Roads Research Articles

Following car reduces motorcycles’ size-arrival effect: A study using online experiments

Many fatal motorcycle accidents occur because car drivers infringe on motorcycle riders' right-of-way. The size-arrival effect refers to observers' tendency to judge larger objects as arriving sooner than smaller objects when estimating an approaching object's arrival time, which is one cause of right-of-way motorcycle accidents. Previous research has focused on a single vehicle that approaches the driver. However, it is also possible that a motorcycle approaches a driver along with other vehicles driving on multiple-lane roads. This paper presents the results of two online experiments; Experiment 1 validated the size-arrival effect when either a car or a motorcycle approached a driver waiting to turn across an intersection; and Experiment 2 investigated the size-arrival effect when a motorcycle and a parallelly driven car simultaneously approached the driver. Participants (n = 1723) in Experiment 1 and (n = 986) in Experiment 2 took part in the study. The results (1) validated the size-arrival effect; drivers accepted a smaller gap for approaching motorcycles than cars; (2) in the present settings drivers made turn decisions based on the distance gap rather than the time-to-arrival gap; (3) driver's acceptance of the gap when facing a motorcycle and a car was comparable to when facing only the car and significantly larger than facing only a motorcycle. These findings indicate that a car driving parallelly or behind a motorcycle reduces the size-arrival effect. These findings provide implications to suggest a safe riding strategy for motorcycle riders. We also suggest that such online experiments would facilitate studying large samples with less effort.

Injury profiles and epidemiology of single vehicle motorcycle fatalities in Tamil Nadu, India, 2009-2017

This article investigates single-vehicle motorcycle crashes and crash risk where a rider is killed or injured in Tamil Nadu, India. Hospital data linked with police-reported crash data, including demographic information, injury characteristics, and environmental and road characteristics, were analysed for all single-vehicle motorcycle crashes between January 2009 and December 2017. A total of 16,541 single-vehicle motorcycle crashes resulted in injuries (fatal, serious, and minor injury) during the period, among which 7,447 were fatal crashes. The majority of fatal crashes involved male motorcyclists aged between 26 and 64 years (5,287). Those aged 18 and 25 years (1,897) were the next most frequent group of fatally injured motorcyclists. Among the fatal injured motorcyclists, 66.6% were wearing a helmet. When reported, 33.8% of motorcyclists did not have a valid driving licence at the time of the incident. Motorcyclists injured most commonly sustained head injuries (51.3%), followed by multiple injuries (20.1%), leg injuries (3.3%), and hand injuries (3.2%). Logistic regression results suggest that specific road characteristics and environmental factors increase the probability of fatal crashes: urban roads, express highways, multiple lane roads, night time, absence of median divider, cross and T-junctions, junctions with four arms, under-aged (<18 years) riders, not holding a valid licence and non-use of helmets. Appropriate countermeasures to mitigate motorcyclist trauma are recommended.

Ultrafine particle exposure for bicycle commutes in rush and non-rush hour traffic: A repeated measures study in Copenhagen, Denmark

Ultrafine particles (UFP), harmful to human health, are emitted at high levels from motorized traffic. Bicycle commuting is increasingly encouraged to reduce traffic emissions and increase physical activity, but higher breathing rates increase inhaled UFP concentrations while in traffic. We assessed exposure to UFP while cycling along a fixed 8.5 km inner-city route in Copenhagen, on weekdays over six weeks (from September to October 2020), during morning and afternoon rush-hour, as well as morning non-rush-hour, traffic time periods starting from 07:45, 15:45, and 09:45 h, respectively. Continuous measurements were made (each second) of particle number concentration (PNC) and location. PNC levels were summarized and compared across time periods. We used generalized additive models to adjust for meteorological factors, weekdays and trends. A total of 61 laps were completed, during 28 days (∼20 per time period). Overall mean PNC was 18,149 pt/cm3 (range 256–999,560 pt/cm3) with no significant difference between morning rush-hour (18003 pt/cm3), afternoon rush-hour (17560 pt/cm3) and late morning commute (17560 pt/cm3) [p = 0.85]. There was substantial spatial variation of UFP exposure along the route with highest PNC levels measured at traffic intersections (∼38,000-42000 pt/cm3), multiple lane roads (∼38,000-40000 pt/cm3) and construction sites (∼44,000-51000 pt/cm3), while lowest levels were measured at smaller streets, areas with open built environment (∼12,000 pt/cm3), as well as at a bus-only zone (∼15,000 pt/cm3). UFP exposure in inner-city Copenhagen did not differ substantially when bicycling in either rush-hour or non-rush-hour, or morning or afternoon, traffic time periods. UFP exposure varied substantially spatially, with highest concentrations around intersections, multiple lane roads, and construction sites. This suggests that exposure to UFP is not necessarily reduced by avoiding rush-hours, but by avoiding sources of pollution along the bicycling route.

Fault Detection Based on Parity Equations in Multiple Lane Road Car-Following Models Using Bayesian Lane Change Estimation

One of the current topics of interest in transportation science is the use of intelligent computation and IoT (Internet of Things) technologies. Researchers have proposed many approaches using these concepts, but the most widely used concept in road traffic modeling at the microscopic level is the car-following model. Knowing that the standard car-following model is single lane-oriented, the purpose of this paper is to present a fault detection analysis of the extension to a multiple lane car-following model that uses the Bayesian reasoning concept to estimate lane change behavior. After the application of the latter model on real traffic data retrieved from inductive loops placed on a road network, fault detection using parity equations was used. The standard car-following model applied separately for each lane showed the ability to perform a lane change action and to incorporate a new vehicle into the current lane. The results will highlight the advantages and the critical points of influence in the use of a multiple lane car-following model based on probabilistic estimated lane changes. Additionally, this research applied fault detection based on parity equations for the proposed model. The purpose was to deliver an overview of the faults introduced by the behavior of vehicles in adjacent lanes on the behavior of the target vehicle.

On performance evaluation of a moving bottleneck on a multiple-lane road with a fixed bottleneck by using numerical simulations with a car-following model

On performance evaluation of a moving bottleneck on a multiple-lane road with a fixed bottleneck by using numerical simulations with a car-following model

Modeling Probabilistic Flooding in VANETs for Optimal Rebroadcast Probabilities

Probabilistic flooding is commonly considered in vehicular ad-hoc networks (VANETs) to alleviate the celebrated broadcast storm problem. Heuristics and simulations have so far proved to be an efficient way with which to design probabilistic information dissemination solutions with demonstrated effectiveness. Supplementing with mathematical analysis for both design and evaluation is expected to generalize the results, and increase the confidence in the proposed solutions. So far, this has been limited due to the difficulties associated with the underlying stochastic dependencies. In this paper, we introduce simple models of single and multiple lane roads to design effective probabilistic flooding schemes for VANETs. The proposed analytical framework offers a novel and generic tool which can cover any number of lanes, vehicle transmission range, and density. We derive difference equations whose solutions yield the probability of all vehicles receiving the emergency warning message as a function of the rebroadcast probability, the number of neighbors of each vehicle, and the dissemination distance. The models are validated using simulations, realistically representing both the traffic and networking aspects, and are used as a baseline to design two schemes for information dissemination which employ probabilistic flooding. The one attempts direct calculation of the required parameters whereas the other adaptively regulates the rebroadcast probability based on the vehicle speed. The schemes are evaluated using simulations and are found to achieve significant performance improvements compared to blind flooding with respect to the achieved reachability, end-to-end delay, and number of rebroadcasts, comparable to the performance achieved by optimal flooding, obtained via brute force.

Accident risk of road and weather conditions on different road types

This study was designed to investigate the relative accident risk of different road weather conditions and combinations of conditions. The study applied a recently developed method which is based on the notion of Palm probability, originating in the theory of random point processes, which in this case corresponds to picking a random vehicle from the traffic. The method consists of calculating the Palm distribution of different conditions and comparing it with the distribution of the same conditions as seen by the accidents. The condition affects the accident risk statistically, when these two distributions differ. The study included all police reported single- and multi-vehicle accidents (N = 10,646) occurring on 43 main roads in Finland during the years 2014–2016. A major contribution of this paper is the demonstration of the method on national scale by using estimated hourly traffic volumes on road segments instead of measured ones, which would have been available for few roads only. Accident risks are commonly examined in relation to traffic volume. This paper includes the speed of the traffic and thus, the paper examines accident risk in relation to the time spent on the road segment in certain conditions. The hour-level weather and road condition data per segment were obtained from nearby road weather stations. The relative accident risks were increased for poor road weather conditions; however, they were highest for icy rain and slippery and very slippery road conditions. When comparing the relative accident risk based on road type, the results showed that the risk in poor weather and road conditions was higher on motorways compared to two-lane and multiple-lane roads even though the overall risk was lower on motorways. Furthermore, the corresponding relative accident risks were generally higher for single-vehicle accidents compared to multi-vehicle accidents.

Routed roads: Probabilistic vision-based place recognition for changing conditions, split streets and varied viewpoints

Vision-based place recognition is becoming an increasingly viable component of navigation systems for autonomous robots and personal aids. However, attaining robustness to variations in environmental conditions—such as time of day, weather and season—and camera viewpoint remains a major challenge. Featureless, sequence-based place recognition techniques have demonstrated promise, but often rely on long image sequences, manually-tuned parameters and exhaustive sequence match searching through multiple locations and image scales. In this paper, we address these deficiencies by implementing a condition-invariant, sequence-based place recognition algorithm suitable for networked environments, such as city streets, and routes with lateral platform shift, such as multiple-lane roads. We achieve this capability by augmenting the traditional 1D image database with a directed graph to describe the branching of contiguous sections of imagery at intersections. A particle filter is then used to efficiently explore these paths, as well as various lateral positions synthesized by rescaling imagery. Our proposed approach eliminates manual tuning of sequence length parameters, improves localization on branched routes, improves overall place recognition accuracy and coverage, and reduces computational requirements. We evaluated the new method against the original SeqSLAM and SMART algorithms on two day–night, road-based datasets and a summer–winter train dataset, where it attained superior precision-recall performance and coverage in all environments. Together, these contributions represent a significant step towards the provision of a robust, near parameter-free condition- and viewpoint-invariant visual place recognition capability for vehicles and robots.

Real-Time Measurement of Link Vehicle Count and Travel Time in a Road Network

A system is described that measures the vehicle count and travel time in the links of a road network. The measurements require matching vehicle signatures recorded by a wireless magnetic sensor network. The matching algorithm is based on a statistical model of the signatures. The model itself is estimated from the data. The approach is first discussed for a single-lane road and extended to multiple-lane roads. The algorithm yields a correct matching rate of 75% for a false matching rate of 5% and reliably estimates the number of vehicles on each link and its travel-time distribution. The system is tested on a 0.9-mi-long segment of San Pablo Avenue, Albany, CA.

Achievement of alternative configurations of vehicles on multiple lanes

Heavy traffic congestion occurs daily at merging sections on a highway. For relieving this congestion, possibility of alternative configuration of vehicles on multiple-lane road at a merging area is discussed in this paper. This is the configuration where no vehicles move aside on the other lane. It has merit in making a smooth merging at an intersection or a junction due to the so-called "zipper effect." We show, by developing a cellular automaton model for multiple lanes, that this configuration is achieved by simple local interactions between vehicles neighboring each other. The degree of the alternative configuration in terms of the spatial increase in parallel driving length is studied by using both numerical simulations and mean-field theory. We successfully construct a theoretical method for calculating this degree of the alternative configuration by using cluster approximation. It is shown that the theoretical results coincide with those of the simulations very well.

Intelligent guidance for headway and lane control

The principal objective of this study is to define techniques for using graphical imagery alone as guidance information for automotive headway and lane control. The goal is to accelerate, steer, and brake an automobile at normal speeds on both limited-access and conventional highways, including entry/exit ramps, multiple-lane roads, and undivided highways, and accounting for mixed traffic, road signs, traffic signals, and unexpected emergencies. It is expected that a system capable of performing these tasks would consist of one or more television cameras, pattern-recognition logic implemented in digital signal processing chips, a rule-based expert system for guidance and control, conventional motion sensors, and actuators for steering, braking, and throttle control.