Single-lane Roads Research Articles

Developmental changes in how pedestrians cross streets in single- vs. dual-lane traffic conditions.

Pedestrian injury is a leading contributor to childhood deaths. This study compared the crossing behaviors of children with adults when crossing in virtual single-lane vs. two-lane traffic conditions. Using a fully immersive virtual reality system, children 7-10 years and adults crossed streets with traffic in one lane (near lane) or in two lanes (near and far lane) with 0 offset so cars from opposing directions arrived at the child's crossing line at the same time. On each trial, pedestrians made a decision on when to cross and then completed the crossing, with measures of attention and crossing behaviors automatically recorded by the system as they did so. In comparison to crossing a single lane of traffic, crossing two-lane roads increased injury risk for all pedestrians, though children experienced significantly greater risk than adults. Children predominantly crossed by stopping before entering the far lane, whereas adults showed greater synchronization of self-movement to traffic flow and more often crossed both lanes without stopping. Children experience more high-risk outcomes than adults when crossing single-lane roads. Crossing two-lane roads elevates risk for pedestrians of both ages, though this risk is significantly greater for child than adult pedestrians. The predominant strategy used to cross two lanes of traffic shows significant developmental changes. Implications for injury prevention are discussed.

Leveraging computer vision for predicting collision risks: a cross-sectional analysis of 2019–2021 fatal collisions in the USA

ObjectiveThe USA has higher rates of fatal motor vehicle collisions than most high-income countries. Previous studies examining the role of the built environment were generally limited to small geographic areas...

Energy-optimal Speed Regulation for CAVs in Urban Mixed Traffic with Induced Lane Changes

Existing urban energy-optimal vehicle speed regulation (eco-driving) studies were mostly carried out on single-lane roads or did not consider lane changes, delivering overidealized eco-driving results. In addition to lane changes that every vehicle may encounter on roads, the eco-driving connected automated vehicles (CAVs) are subject to induced types of lane changes of manually-driven vehicles (MVs), a cut-in of an MV to the front of a CAV from an adjacent lane, and an escape of an MV from behind a CAV to an adjacent lane. So far, very few studies have considered the impact of lane changes while designing the ecodriving strategies and evaluated the eco-driving performance with respect to lane-changing disturbances in credible simulation environments; and almost no existing work has addressed both lateral and longitudinal disturbances for eco-driving. This paper has developed a generic and deployable eco-driving strategy for CAVs under a unified rolling-horizon optimal control framework that can deal with both longitudinal and lateral disturbances of MVs without assuming communications between CAVs and MVs. The performance of this disturbance-rejection eco-driving strategy was thoroughly evaluated with regard to a multilane urban network, using the microscopic traffic simulator SUMO. The strategy was demonstrated very effective and robust, and similar results were little reported before.

Environmental assessment of road freight transport services beyond the tank-to-wheels analysis based on LCA

Reducing environmental impacts in transport motivates many studies to offer more sustainable freight services. However, most methodologies focus on impacts from fuel consumption, and approaches trying to integrate other transport components have not facilitated its application to actual and specific transport services. In this study, we present a harmonized approach to address the transport services with a holistic way to increase the knowledge about hotspots of the transport sector based on the life cycle assessment methodology. In this framework, vehicle manufacturing, fuel production, and infrastructure construction are the key transport components around the traffic process. Besides fuel usage, the operation and maintenance of vehicles and infrastructures are also included. We developed a tool to create the life cycle inventories for each transport component to be applied to specific transport services in any location with a comprehensive view and low uncertainty in the results. This approach was applied to road-freight services in Colombia, Malaysia, and Spain. The main results showed the nature and origin of the environmental impacts, which are highly influenced by the emissions control technologies, road characteristics, and traffic volume. The contribution of atmospheric pollutants per tonne-km can decrease by a quarter when Euro VI trucks on highways instead of conventional trucks on single-lane roads are used. However, these contributions are highly affected by fuel production due to the origin of biofuels. The proposed methodology provides relevant information to estimate transport impacts in the life cycle assessment of products with superior precision and identify strategies for systemically improving sustainability.

A General Framework for Decentralized Safe Optimal Control of Connected and Automated Vehicles in Multi-Lane Signal-Free Intersections

We address the problem of optimally controlling Connected and Automated Vehicles (CAVs) arriving from four multi-lane roads at a signal-free intersection where they conflict in terms of safely crossing (including turns) with no collision. The objective is to jointly minimize the travel time and energy consumption of each CAV while ensuring safety. This problem was solved in prior work for single-lane roads. A direct extension to multiple lanes on each road is limited by the computational complexity required to obtain an explicit optimal control solution. Instead, we propose a general framework that first converts a multi-lane intersection problem into a decentralized optimal control problem for each CAV with less conservative safety constraints than prior work. We then employ a method combining optimal control and control barrier functions, which has been shown to efficiently track tractable unconstrained optimal CAV trajectories while also guaranteeing the satisfaction of all constraints. Simulation examples are included to show the effectiveness of the proposed framework under symmetric and asymmetric intersection geometries and different CAV sequencing policies.

Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes.

Built environment neighborhood characteristics are difficult to measure and assess on a large scale. Consequently, there is a lack of sufficient data that can help us investigate neighborhood characteristics as structural determinants of health on a national level. The objective of this study is to utilize publicly available Google Street View images as a data source for characterizing built environments and to examine the influence of built environments on chronic diseases and health behaviors in the United States. Data were collected by processing 164 million Google Street View images from November 2019 across the United States. Convolutional Neural Networks, a class of multi-layer deep neural networks, were used to extract features of the built environment. Validation analyses found accuracies of 82% or higher across neighborhood characteristics. In regression analyses controlling for census tract sociodemographics, we find that single-lane roads (an indicator of lower urban development) were linked with chronic conditions and worse mental health. Walkability and urbanicity indicators such as crosswalks, sidewalks, and two or more cars were associated with better health, including reduction in depression, obesity, high blood pressure, and high cholesterol. Street signs and streetlights were also found to be associated with decreased chronic conditions. Chain link fence (physical disorder indicator) was generally associated with poorer mental health. Living in neighborhoods with a built environment that supports social interaction and physical activity can lead to positive health outcomes. Computer vision models using manually annotated Google Street View images as a training dataset were able to accurately identify neighborhood built environment characteristics. These methods increases the feasibility, scale, and efficiency of neighborhood studies on health.

Compatibility between delay functions and highway capacity manual on Iraqi highways

Abstract Volume delay functions (VDFs) are mathematical relationships used by the traffic allocation step of demand forecasting models to take into account the effect of increased traffic flow on the time spent to travel each possible route between different travel sources and destinations. The VDF is usually applied in static traffic assignment to describe the resultant link travel times, as a function of flow and capacity and free-flow travel time. This study aims to investigate the interface between the delay functions used by demand forecasting models and the highway capacity manual (HCM) model flow-speed relationships. The most commonly used VDFs in transport demand modeling packages in the literature were identified. The Bureau of Public Roads (BPR), conical functions (CF), Akçik and Troutbeck function (ATF), and delay logistics function (LF) were described. The four VDFs and the current HCM models were calibrated for the Iraqi road environment, and their compatibilities were examined. Results show that the best adjustments were obtained using the BPR function (quadratic error 0–0.012) and LF (quadratic error 0–0.002). The roles of CF and ATF were used with care, as both appear to neglect the delay in the condition of small to medium traffic patterns typical to country roads. Particularly, in response to single-lane roads, the LF has proven to be useful due to its potential to represent significant delays for low traffic flows and simultaneously produce more delays in congestion conditions; furthermore, the effect of flow as well as intersection spacing is obviously nonlinear. As flow reaches 600 pcu/h/lane, running time increases quickly. With more intersections per kilometer, the impact is obviously greater.

Google Street View Images as Predictors of Patient Health Outcomes, 2017-2019.

Collecting neighborhood data can both be time- and resource-intensive, especially across broad geographies. In this study, we leveraged 1.4 million publicly available Google Street View (GSV) images from Utah to construct indicators of the neighborhood built environment and evaluate their associations with 2017–2019 health outcomes of approximately one-third of the population living in Utah. The use of electronic medical records allows for the assessment of associations between neighborhood characteristics and individual-level health outcomes while controlling for predisposing factors, which distinguishes this study from previous GSV studies that were ecological in nature. Among 938,085 adult patients, we found that individuals living in communities in the highest tertiles of green streets and non-single-family homes have 10–27% lower diabetes, uncontrolled diabetes, hypertension, and obesity, but higher substance use disorders—controlling for age, White race, Hispanic ethnicity, religion, marital status, health insurance, and area deprivation index. Conversely, the presence of visible utility wires overhead was associated with 5–10% more diabetes, uncontrolled diabetes, hypertension, obesity, and substance use disorders. Our study found that non-single-family and green streets were related to a lower prevalence of chronic conditions, while visible utility wires and single-lane roads were connected with a higher burden of chronic conditions. These contextual characteristics can better help healthcare organizations understand the drivers of their patients’ health by further considering patients’ residential environments, which present both risks and resources.

Are bicycle lanes effective? The relationship between passing distance and road characteristics

An on-road observational study of 162 cyclists was conducted in the Australian cities of Melbourne, Perth, Geelong, and Bendigo. Participants had a distance sensor and two video cameras fitted to their bicycle for two weeks while they cycled on their usual routes, producing 46,769 events where a motor vehicle passed a bicycle. This was the largest study to-date to record passing behavior on public roads, and a large number of road and traffic attributes that might affect passing distance were included in the analysis.When drivers pass cyclists on roads with painted bicycle lanes, they tend to give more space than on roads without bicycle lanes. This is true even when controlling for the space available on the roadway. Drivers also travel in a more predictable fashion, with less variability in passing distances when a bicycle lane is present. Protected bicycle lanes completely remove the risk of passing events less than 1 m. However, where it is not possible to build a protected bicycle lane it is preferable to have a painted bicycle lane than no bicycle lane at all.Other protective factors include: wider lanes, single lane roads, smaller vehicles, and the removal of on-street parking.

Leveraging 31 Million Google Street View Images to Characterize Built Environments and Examine County Health Outcomes.

Built environments can affect health, but data in many geographic areas are limited. We used a big data source to create national indicators of neighborhood quality and assess their associations with health. We leveraged computer vision and Google Street View images accessed from December 15, 2017, through July 17, 2018, to detect features of the built environment (presence of a crosswalk, non-single-family home, single-lane roads, and visible utility wires) for 2916 US counties. We used multivariate linear regression models to determine associations between features of the built environment and county-level health outcomes (prevalence of adult obesity, prevalence of diabetes, physical inactivity, frequent physical and mental distress, poor or fair self-rated health, and premature death [in years of potential life lost]). Compared with counties with the least number of crosswalks, counties with the most crosswalks were associated with decreases of 1.3%, 2.7%, and 1.3% of adult obesity, physical inactivity, and fair or poor self-rated health, respectively, and 477 fewer years of potential life lost before age 75 (per 100 000 population). The presence of non-single-family homes was associated with lower levels of all health outcomes except for premature death. The presence of single-lane roads was associated with an increase in physical inactivity, frequent physical distress, and fair or poor self-rated health. Visible utility wires were associated with increases in adult obesity, diabetes, physical and mental distress, and fair or poor self-rated health. The use of computer vision and big data image sources makes possible national studies of the built environment's effects on health, producing data and results that may inform national and local decision-making.

Risky riding behaviours among motorcyclists in Malaysia: A roadside survey

Motorcyclists are over-represented in fatal crashes in Low- and Middle-Income Countries (LMICs). In Malaysia, motorcyclists comprise about 60% of all fatalities in road crashes. However, the prevalence and determinants of risky riding behaviours have been rarely studied in the country. This study aims to investigate motorcycle-related risky riding behaviours at signalised intersections. A total of 72,377 observations were made during six days at six different signalised intersections in Terengganu, Malaysia. Four risky riding behaviours were observed together (i.e. helmet non-use, red-light running, mobile phone use, turn signal neglect) along with additional demographic and contextual factors. The most prevalent risky riding behaviour was turn signal neglect (29.7% of all observations), and the least prevalent was mobile phone while riding (0.2% of all observations). Four logistic regressions were fitted to predict the four risky riding behaviours using the demographic and contextual explanatory factors. The results suggest that helmet non-use increases among female riders, riders wearing industrial uniforms, carrying passengers, riding during the weekend, during off-peak hours, during clear weather, at T-junctions, on multi-lane roads, and on minor road approaches. Red light running increases among female riders, riders wearing industrial uniforms, carrying passengers, during clear weather, at T-junctions, on multi-lane roads, and on major road approaches. Mobile phone use increases among female riders, riders wearing industrial uniforms, carrying passengers, and at cross-junctions. Turn signal neglect increases among male riders, riders not wearing industrial uniforms, solo riders, on weekends, during off-peak hours, during clear weather, on single-lane roads, and on minor road approaches. The findings of this study have significant implications for the development of targeted countermeasures such as education programs and road policing.

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents’ risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.

Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States.

Previous studies have demonstrated that there is a high possibility that the presence of certain built environment characteristics can influence health outcomes, especially those related to obesity and physical activity. We examined the associations between select neighborhood built environment indicators (crosswalks, non-single family home buildings, single-lane roads, and visible wires), and health outcomes, including obesity, diabetes, cardiovascular disease, and premature mortality, at the state level. We utilized 31,247,167 images collected from Google Street View to create indicators for neighborhood built environment characteristics using deep learning techniques. Adjusted linear regression models were used to estimate the associations between aggregated built environment indicators and state-level health outcomes. Our results indicated that the presence of a crosswalk was associated with reductions in obesity and premature mortality. Visible wires were associated with increased obesity, decreased physical activity, and increases in premature mortality, diabetes mortality, and cardiovascular mortality (however, these results were not significant). Non-single family homes were associated with decreased diabetes and premature mortality, as well as increased physical activity and park and recreational access. Single-lane roads were associated with increased obesity and decreased park access. The findings of our study demonstrated that built environment features may be associated with a variety of adverse health outcomes.

Analysis of factors associated with exceeding lawful speed traffic violations in Indian metropolitan city

Exceeding lawful speed is a single most important driver error responsible for causing road accidents and has accounted for 66.5% of driver error related faults in India. In the present study, speeding behaviour of drivers was examined to identify significant risk factors associated with this traffic violation. Risk factors including driver characteristics, vehicle, environmental and crash/accident attributes were considered. Most recent crash data (N = 5254) of 2 years (2015–2016) for the Indian metropolitan city, Chennai was analysed from the Government of Tamil Nadu database RADMS (Road Accident Data Management System). Contingency tables were constructed to assess the association between the risk factors and speeding behaviour at a 10% significance level. To further estimate the effect of different predictor variables on the likelihood of occurrence of speeding, logistic regression analyses were carried out. Results showed that (i) male drivers, (ii) drivers without a valid driving licence, (iii) single-lane roads, (iv) uncontrolled junction roads, (v) all type of vehicles, (vi) presence of central dividers, (vii) daylight were associated with speeding-related accidents. Results of the present study can help transportation officials to frame policies to mitigate accident and deaths due to speeding.

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.

Traffic flow in the vicinity of crosswalks on a provincial highway in China

In this paper, motorized traffic flow in the vicinity of an unsignalized crosswalk on a provincial highway in China was studied. Based on the Kerner–Klenov–Schreckenberg–Wolf (KKSW) three-phase cellular automaton (CA) traffic flow model for single-lane roads, the cars’ moving rules when there were pedestrians walking on the crosswalk was redefined. A series of simulations were carried out to reveal the cars’ travel process and the influence of pedestrians crossing behavior on traffic flow. The simulations results show the redefined model can be used to study not only the characteristics of traffic flow in the vicinity of crosswalks on provincial highways but also some complex traffic problems such as traffic breakdown, synchronized flow, and moving jams. Moreover, some suggestions about signal control for the pedestrians crossing in the future were presented. Thus, this work is helpful for managing traffic flow and improving road safety and capacity in the vicinity of crosswalks on provincial highways in China.

A Low Cost Vision-Based Road-Following System for Mobile Robots

Navigation is necessary for autonomous mobile robots that need to track the roads in outdoor environments. These functions could be achieved by fusing data from costly sensors, such as GPS/IMU, lasers and cameras. In this paper, we propose a novel method for road detection and road following without prior knowledge, which is more suitable with small single lane roads. The proposed system consists of a road detection system and road tracking system. A color-based road detector and a texture line detector are designed separately and fused to track the target in the road detection system. The top middle area of the road detection result is regarded as the road-following target and is delivered to the road tracking system for the robot. The road tracking system maps the tracking position in camera coordinates to position in world coordinates, which is used to calculate the control commands by the traditional tracking controllers. The robustness of the system is enhanced with the development of an Unscented Kalman Filter (UKF). The UKF estimates the best road borders from the measurement and presents a smooth road transition between frame to frame, especially in situations such as occlusion or discontinuous roads. The system is tested to achieve a recognition rate of about 98.7% under regular illumination conditions and with minimal road-following error within a variety of environments under various lighting conditions.

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.

Situation of Land Transportation in Nepal

There is a close relation between increment of transportation (roads and vehicles) and socio-economic development of the nation however, present condition of Nepal is discouraging. The study deals with present situation of land transportation, its problems with suggestions. An attempt has been made to point out causes and nature of road accidents, problems faced by general people and businessmen during travel and transportation of cargo. This study reveals that out of 29157 kilometers available road network only 12305 kilometers (42.20 %) road was black topped and the condition of road was miserable because there was no proper and regular repair and maintenance. Graveled roads were in very poor condition and in most of the roads and bridges were not constructed. Earthen roads were not suitable for travelling in rainy season. The buses run on long routes were found violating norm of keeping double drivers, the speed of vehicle remained higher than limit. They unnecessarily used loud or pressurize horn which had created noise pollution. The vehicles moved on graveled and earthen roads had been operating in poor condition. Vehicles were very old and damaged and only a few vehicles moved on certain routes. Vehicle accident incidences had frequently occurred in hilly areas where vehicles plunged down from the road and number of fatalities and injuries were found in double digit. The major reasons of accidents were high speed of vehicles, steering or brake failure, untrained and intoxicated drivers, driving by cleaner/conductor, continuous long duty hours of drivers, poor condition of vehicle, muddy and slippery roads, haphazard overtaking practice on single lane roads, lack of concentration of drivers on driving , non-observance of safety measures, traffic rules and signs, drove vehicle without wearing seat belt/helmet, kept more passengers than allowed/available seats, hung passengers on gate, backside and roof, loaded cargo beyond the limited weight and height respectively.

Effects of different road conditions on rectal temperature, behaviour and traumatic injuries during transportation of different crosses of temperate/tropical breeds of heifers

One important and inevitable aspect of animal production is transportation. The effects of different types of road transportation on rectal temperature (RT), behaviour and traumatic injuries were assessed in 60 crosses of 50/50% temperate/tropical breeds of heifers. The heifers, comprised 20 crosses, each of Brahman/Gudali, Friesian/White Fulani and Simmental/Gudali breeds, were transported for 6 h; first, for 2 h through rough untarred road, then another 2 h each through an asphalt dual- and single-carriage roads, respectively. The results showed that transportation through untarred road induced significant (P < 0.05) increase in RT values, especially in Friesian/White Fulani breed. All (100%) the cattle fell down, vocalised and urinated/defaecated at least three times during the first 30 min of transportation through rough untarred road. The mean frequencies of falls, vocalisation and urinating/defaecating were 7.8 ± 1.2, 4.3 ± 0.6 and 3.2 ± 0.4, respectively. Transportation on double- and single-lane asphalt roads had no effect on the variables measured. The cattle stood for 5 h 8 min out of the 6-h transportation time and majority stood parallel to the direction of motion. Two Friesian/White Fulani crosses sustained injuries during loading and transportation on rough road. The proportion of time the heifers spent standing was low (P < 0.05), whereas more time was spent eating and drinking 3 h post-transportation, compared with pre-transportation. Friesian/White Fulani breed stood longer (P < 0.05), but spent less time eating and drinking in both pre- and post-transportation periods. In conclusion, road transportation of temperate/tropical crosses of heifers along asphalt road did not significantly affect their RT and behaviour, however, when transported along untarred road the RT and behaviour of the cattle may be compromised.