Drivers In Urban Areas Research Articles

Stability of Adaptive Cruise Control of Automated Vehicle Platoon under Constant Time Headway Policy

Traffic congestion is becoming more prevalent as the number of vehicles on the roads continues to rise.To shorten travel times and enhance driver comfort, a range of Advanced Driver Assistance Systems (ADAS) has been developed to assist drivers in urban areas and on highways. The demand for increasing road capacity has introduced the concept of vehicle platooning, where the use of the Adaptive Cruise Control (ACC) system is a key function within the advanced driver assistance (ADAS) technology, this technology manages the vehicle's longitu-dinal control in specific driving conditions. Cars equipped with ACC can efficiently maintain a set distance from the vehicle ahead, easing the driver’s workload while offering advantages such as improved road capacity, lower fuel consumption, and reduced pollution emissions. However, they can be susceptible to string instability, resulting in the amplification of oscillations caused by speed variations along the platoon's rear. This paper presents a string stability analysis of car platoons equipped with ACC system based on a heuristic method by choosing of the constant time headway policy (CTHP). The constant time headway policy selection for string stability is based on the Nyquist diagram of the transfer function of the spacing errors between two cars. A platoon operated by using distance-based ACC control structures is implemented. These structures employ a linear quadratic regulator (LQR) using a dual integrator. The simulation results were acquired by modeling and simulating the studied platoon within Matlab/Simulink.

Prevalence of motorcycle accidents among food delivery drivers and its relation to knowledge, attitudes, and practices in urban areas in Bangkok, Thailand.

Food delivery drivers are at increased risk of motorcycle accidents every year. This study investigated the prevalence of motorcycle accidents among food delivery drivers related to the knowledge, attitudes, and practices in urban areas in Bangkok, Thailand. This was a cross-sectional online survey on motorcycle accidents was distributed among food delivery drivers in urban areas in Bangkok, Thailand from February-March 2023. The study involved 809 participants aged 18 years. A binary logistic regression was conducted to test the association between variable factors and motorcycle accidents, and a Spearman's analysis was employed to test the correlations between motorcycle accidents and knowledge, attitude, and practice scores. The study found the prevalence of accidents associated with food delivery drivers was 284 (35.1%). The results of the binary logistic regression analysis found that those who drive on an average of more than 16 rounds per day were significantly associated with motorcycle accidents (OR = 2.128, 95%CI 1.503-3.013), and those who had followed improper driving practices were significantly associated with motorcycle accidents (OR = 1.754, 95%CI 1.117-2.752). The correlation analysis found the knowledge score positive significantly with the practice score (r = 0.269, p-value < 0.01) and the attitudes score positive significantly with the practice score (r = 0.436, p-value < 0.01). This study shows the knowledge level correlated with the practice score regarding such accidents. Therefore, our study needs more longitudinal study to identify which variable factors influence motorcycle accidents among FDDs. The current study suggests that the management of traffic safety on urban roads is significantly affected by food delivery services. Thus, this study can be used as baseline data to devise systematic measures to prevent motorcycle crashes of food deivery workers.

Summer electricity consumption and its drivers in urban areas

The issue of energy consumption is receiving increasing attention due to the growth of the global population and urbanization rate. This study comprehensively analyzed the independent effects, spatial varying effects, and interactive effects of potential factors on summer electricity consumption (EC) by using the geographical detector and geographically weighted regression model. Finally, the study area was partitioned into clusters based on the GWR local coefficients to provide zonal customized policy implications. Results found that floor area ratio (FAR) was the key factor that influenced EC. As for independent effects, FAR independently explained 36.3% of the variance of the EC. As for spatial varying effects, the association between FAR and EC was found to be spatially non-stationary. This association was positive in high FAR regions and vice versa in low FAR regions. As for interaction effects, FAR ∩ land surface temperature was identified as the factor pair with the highest interactive explanatory power. Finally, the study area was divided into four clusters, and customized policy implications were proposed based on the local relationship between EC and factors. This study provides a new perspective for understanding urban energy consumption, which will contribute to sustainable development and energy consumption management.

Heterogeneous Algorithm for Efficient-Path Detection and Congestion Avoidance for a Vehicular-Management System

Finding reliable and efficient routes is a persistent problem in megacities. To address this problem, several algorithms have been proposed. However, there are still areas of research that require attention. Many traffic-related problems can be resolved with the help of smart cities that incorporate the Internet of Vehicles (IoV). On the other hand, due to rapid increases in the population and automobiles, traffic congestion has become a serious concern. This paper presents a heterogeneous algorithm called ant-colony optimization with pheromone termite (ACO-PT), which combines two state-of-the-art algorithms, pheromone termite (PT) and ant-colony optimization (ACO), to address efficient routing to improve energy efficiency, increase throughput, and shorten end-to-end latency. The ACO-PT algorithm seeks to provide an effective shortest path from a source to a destination for drivers in urban areas. Vehicle congestion is a severe issue in urban areas. To address this issue, a congestion-avoidance module is added to handle potential overcrowding. Automatic vehicle detection has also been a challenging issue in vehicle management. To address this issue, an automatic-vehicle-detection (AVD) module is employed with ACO-PT. The effectiveness of the proposed ACO-PT algorithm is demonstrated experimentally using network simulator-3 (NS-3) and Simulation of Urban Mobility (SUMO). Our proposed algorithm is compared with three cutting-edge algorithms. The results demonstrate that the proposed ACO-PT algorithm is superior to earlier algorithms in terms of energy usage, end-to-end delay, and throughput.

Real-Time Self-Adaptive Traffic Management System for Optimal Vehicular Navigation in Modern Cities

The increase in private car usage in cities has led to limited knowledge and uncertainty about traffic flow. This results in difficulties in addressing traffic congestion. This study proposes a novel technique for dynamically calculating the shortest route based on the costs of the most optimal roads and nodes using instances of road graphs at different timeslots to help minimize congestion for actual drivers in urban areas. The first phase of the study involved reducing traffic congestion in one city. The data were collected using a mobile application installed on more than 10 taxi drivers’ phones, capturing data at different timeslots. Based on the results, the shortest path was proposed for each timeslot. The proposed technique was effective in reducing traffic congestion in the city. To test the effectiveness of the proposed technique in other cities, the second phase of the study involved extending the proposed technique to another city using a self-adaptive system based on a similarity approach regarding the structures and sub-regions of the two cities. The results showed that the proposed technique can be successfully applied to different cities with similar urban structures and traffic regulations. The proposed technique offers an innovative approach to reducing traffic congestion in urban areas. It leverages dynamic calculation of the shortest route and utilizes instances of road graphs to optimize traffic flow. By successfully implementing this approach, we can improve journey times and reduce fuel consumption, pollution, and other operating costs, which will contribute to a better quality of urban life.

Walkability Drivers for Sustainable Cities: a Pedestrian Behavior Survey

The growth of cities often disordered brings several mobility challenges, both in guaranteeing public transport and creating pedestrian-friendly urban spaces. This paper aims to identify the walkability drivers in urban areas and investigate walkability behavior. The survey was carried out in southern Brazil, with a sample of 439 respondents. Data were subject to factorial analysis, regression analysis, and variance analysis. The analysis revealed four walkability drivers that explain pedestrian behavior: (i) infrastructure, (ii) traffic conditions and behavior, (iii) built and natural environments, and (iv) social relations. The results show that built and natural environments and social relations were the most significant aspects in explaining walkability’s overall perception. Concerning socio-demographic aspects, the study points out that (i) men feel safer than women when walking in the neighborhood; (ii) the presence of trees and attractive places along the street increases the walking behavior of non-sedentary people; and (iii) the perception of the neighborhood’s beauty increases with age. The research provides contributions regarding understanding the interrelated aspects of walkability behavior. Finally, the study offers relevant evidence for social researchers and urban planners by identifying walkability drivers and supporting political and academic debate elements.

Exploring the Role of Social Network Structure in Disease Risk among U.S. Long-haul Truck Drivers in Urban Areas.

Objective: Using mixed methods, we explored properties of long-haul truckers' social networks potentially influencing STI/BBI acquisition and transmission. Methods: We recruited inner-city drug and sex network members (N = 88) for interviews. Blood and urine samples and vaginal swabs were collected to test for STIs/BBIs. Data were collected on participants' role in the network (trucker, sex worker, or intermediary), sexual and substance-use behaviors, and dyadic relationships with drug and/or sex contacts. We analyzed network data using UCINET. Results: Data revealed 2 major network clusters (58 male truckers, 6 male intermediaries, and 24 female sex workers; 27.3% STI/BBI positive). Overall, 18.8% of network members had more than one type of risky relationship with the same person (multiplexity), 11.4% of dyads were between 2 STI/ BBI positive people (assortative mixing), 36.4% were between one STI/BBI positive person and one negative person (disassortative mixing), 44.3% of people were connected to more than one person who was STI/BBI positive (concurrency), and 62.5% of nodes were just one path removed from an STI/BBI positive individual (bridging). Conclusion: Despite only 27.3% of the network being STI/BBI positive, our results revealed network characteristics (and potential intervention points) that amplify risk of disease spread within trucker-centered networks.

A Cross Sectional Study on Assessment of Stress among Auto- rickshaw Drivers in Urban Areas of Raichur

Introduction: Auto rickshaw is one of the main mode of public transport in the urban and semi urbanregions. Auto rickshaw drivers may suffer from some kind of stress due to following reasons like finance,driving, health related issues, family issues, marital issues etc., all this may pose risk to the driver andfellow passengers. Objectives: The present study was conducted to determine the stress and its associationwith various factors. Methodology: A cross-sectional study was conducted in the urban areas of Raichur.Different auto-rickshaw stands were randomly selected from different parts of the city. 206 auto-rickshawdrivers were selected by simple random sampling technique. The present study was carried out from Aprilto July 2019. Stress was assessed using perceived stress scale technique. Data was analysed using Epi infoV7.0 software. Results: Out of 206 auto-rickshaw drivers, the prevalence of stress among auto-rickshawdrivers was 29.61%, in which majority (17.96%) had moderate stress, followed by 11.65% had mild stress.Stress has highly significant association between age group and years of driving. Conclusion: This studyshows that the majority of risk factors are modifiable and preventable. Hence creating awareness is essential.

Do commercial vehicles cruise for parking? Empirical evidence from Seattle

Parking cruising is a well-known phenomenon in passenger transportation, and a significant source of congestion and pollution in urban areas. While urban commercial vehicles are known to travel longer distances and to stop more frequently than passenger vehicles, little is known about their parking cruising behavior, nor how parking infrastructure affect such behavior.In this study we propose a simple method to quantitively explore the parking cruising behavior of commercial vehicle drivers in urban areas using widely available GPS data, and how urban transport infrastructure impacts parking cruising times.We apply the method to a sample of 2900 trips performed by a fleet of commercial vehicles, delivering and picking up parcels in Seattle downtown. We obtain an average estimated parking cruising time of 2.3 min per trip, contributing on average for 28 percent of total trip time. We also found that cruising for parking decreased as more curb-space was allocated to commercial vehicles load zones and paid parking and as more off-street parking areas were available at trip destinations, whereas it increased as more curb space was allocated to bus zone.

Do vehicle efficiency improvements lead to energy savings? The rebound effect in Great Britain

Abstract Fuel efficiency improvements in vehicles reduce the cost of travel, which could stimulate drivers to travel further limiting energy savings. Estimates of this effect, known as the rebound effect, have varied widely, partly due to data constraints and a reliance upon highly aggregated government statistics. This paper instead uses a dataset of over 275 million vehicle roadworthiness tests. The high level of detail in our dataset can reveal, for the first time, how the response to changes in travel costs may differ across types of vehicles and socio-economic areas in Great Britain. We find that the rebound effect in Great Britain is just 4.6%, meaning efficiency improvements are unlikely to stimulate increased mileage in the short-run. We find that larger, less fuel efficient vehicles are more responsive to fuel price changes than smaller vehicles and that drivers in urban areas are more responsive to fuel price changes than drivers in rural areas. Our findings shed light on the effects that policies such as fuel taxation and fuel economy standards may have on vehicle mileage. This has implications for both CO2 emissions savings and social equity.

Analysis of traffic accidents of young drivers in urban areas and measures to increase safety

Traffic accidents are considered the first cause of death of young people, that is, young drivers. Therefore, this problem needs to be given a lot of attention. Traffic accident which include young drivers must be analyzed as an element of the system, and it is necessary to analyze and investigate potential solutions and improve all elements in order to improve the system itself. In the Republic of Croatia, for the first time in 2004, the term "YOUNG DRIVER" as a driver of a motor vehicle up to the age of 24, who holds a drivers license issued in the Republic of Croatia, was introduced into the Road Traffic Safety Act. The share of young drivers in the population in the Republic of Croatia is 4.3%. Young drivers make up about eight percent of road users, causing a total of 9.6% of traffic accidents, or 12.5% of those with fatalities. This paper will deal with traffic safety in other countries, especially in the surrounding countries. According to statistics related to road accidents, young drivers are over-represented and are at greater risk to themselves, passengers and other participants than other drivers. There is a need to respond in a timely manner and to adopt optimal measures to improve the safety of young drivers in road traffic and thus for the safety of other road users. The aim of this paper is to outline measures that would reduce the number of road accidents involving young drivers, especially in urban areas.

Decision Tree Ensemble Method for Analyzing Traffic Accidents of Novice Drivers in Urban Areas.

Presently, there is a critical need to analyze traffic accidents in order to mitigate their terrible economic and human impact. Most accidents occur in urban areas. Furthermore, driving experience has an important effect on accident analysis, since inexperienced drivers are more likely to suffer fatal injuries. This work studies the injury severity produced by accidents that involve inexperienced drivers in urban areas. The analysis was based on data provided by the Spanish General Traffic Directorate. The information root node variation (IRNV) method (based on decision trees) was used to get a rule set that provides useful information about the most probable causes of fatalities in accidents involving inexperienced drivers in urban areas. This may prove useful knowledge in preventing this kind of accidents and/or mitigating their consequences.

Dimensionality Reduction to Reveal Urban Truck Driver Activity Patterns

This paper studies the activity profiles of truck drivers in urban areas. Finding repeating dynamical patterns is important in understanding freight behaviors, and aids freight-friendly planning. In the digital age, data on truck drivers is becoming more available with heterogeneous demographic and work profiles. By synthesizing such pervasive data and applying machine learning concepts, this paper proposes to identify signature travel activity patterns via dimensionality reduction. Based on driver survey data, truck drivers’ behaviors are represented as longitudinal activity sequences. Dimensionality reduction and activity reconstruction via principal components analysis (PCA), logistic PCA, and autoencoder were conducted to reveal fundamental activity features and approximate the underlying data-generating function. In the driver survey dataset, 243 truck drivers in Singapore reported their daily activities for 1,099 weekdays. This study found that PCA produced the most faithful reconstruction of drivers’ activities. When projecting the input data down from 2,592 to 82 dimensions, PCA explained 77% of variances with a reconstruction error of 0.99%. Logistic PCA is a useful extension of PCA to study the pattern of a single activity. It captures the variation of infrequent activities such as truck queuing, which PCA fails to reconstruct. Autoencoder was found to be more powerful than PCA in reconstructing activities – with 1% of original dimensions, it reconstructed the activities with an error rate of 1.24%. Moreover, when implemented as a variational autoencoder, autoencoder generated realistic-looking samples of driver activities. The top three most distinctive activity patterns of Singapore truck drivers are reported using PCA.

Urban impacts across realms: Making the case for inter-realm monitoring and management

Burgeoning populations and the increasing concentration of humans in urban areas have resulted in extensive and increasing degradation and destruction of natural ecosystems. The multitude of impacts and their drivers in urban areas across realms are often studied at local scales, but there is regularly a mismatch between the spatial extent of the impacts and that of the pressures driving those impacts. For example, most human activities occur on land and therefore disturb terrestrial habitats (intrinsic impacts), but their impacts can also extend to the atmosphere and aquatic realms (extrinsic impacts). Management of urban impacts is often designed at local scales and aims to control local pressures, mostly overlooking pressures originating outside the ‘managed’ area. This is often due to jurisdictional barriers but can also result from the lack of knowledge and recognition among scientists and managers of larger scale pressures. With the aim to highlight the importance of ameliorating extrinsic impacts for holistic management of urban areas, this paper discusses the range and extent of extrinsic impacts produced by the most common pressures in urban environments. We discuss that the terrestrial realm is a ‘net-donor’ of impacts, as most human activities occur on land and the resulting impacts are transferred to aquatic and atmospheric realms. However, activities in aquatic realms can result in impacts on land. We conclude that, to achieve effective management strategies, greater collaboration is needed between scientists and managers focussing on different realms and regions and we present suggestions for approaches to achieve this.

A Collaborative Method for Route Discovery Using Taxi Drivers’ Experience and Preferences

This paper presents a collaborative route discovery method that leverages the experience and preferences of taxi drivers in urban areas. The proposed method is mainly comprised of two phases: collaborative preference discovery (CPD) and intelligent driver network generation (IDNG). In the first phase, given an origin-destination (O-D) pair and provided that the cluster is a road segment set within a time-reachable range, we propose CPD which involves cluster-to-cluster retrieval to capture the top-k routes that are not only frequently traversed by taxis but also neighboring to the O-D pair. In the second phase, to support route computation, an IDNG algorithm is devised to generate an experiential graph for each specific O-D pair. In empirical studies, using the period-based experiential route database, sensitivity analysis is employed to select optimal parameters of intelligent driver networks. The results demonstrate that the routes recommended by our collaborative method are much more reliable than those of the shortest-path method with respect to the variance of travel time. Moreover, the recommended routes are traversed more frequently than those of the fastest-path and the shortest-path methods, while the travel time and route lengths of our routes are approximately equal to those of the conventional methods.

Comparison of Bus Driving Control Based on Working Shift Hour and Impact on Bus Fuel Consumption

This study is aimed to investigate and compare how the work schedule of bus drivers in urban areas can have impact on bus drivers' control on speed and acceleration. This study also assesses the influence of bus driving behavior on fuel consumption. The work schedule assessed in this research is morning shift (6.00 am to 3.00 pm), afternoon shift (3.00 pm to midnight) and direct shift (6.00 am to midnight). The result from this study indicated that for all driving shifts, drivers spent more than 70% of their driving time for accelerating and braking. Drivers were also significantly driving faster and applying sharper acceleration when accelerating and braking during morning shift. The variation of bus driving behavior between different working shift hours were found to influence bus fuel consumption significantly.

Inequity in health: older rural driving and dementia

The number of drivers with dementia is expected to increase exponentially over the coming decades. Most individuals with moderate-to-severe dementia (table 1) are unfit to drive.1 Drivers with moderate-to-severe dementia...

Norwegian Roadside Survey of Alcohol and Drug Use by Drivers (2008–2009)

Objective: To examine alcohol and drug use among random drivers in different regions of Norway by analyzing oral fluid, compare drivers in urban and rural areas, compare with results from the roadside survey in southeastern Norway in 2005–2006, and roughly estimate the prevalence of driving with blood drug concentrations above the new Norwegian legislative limits among random drivers. This roadside survey was part of the European DRUID (Driving Under the Influence of Drugs, Alcohol and Medicines) Project. Methods: Drivers were selected for a voluntary and anonymous study using a stratified multistage cluster sampling procedure in collaboration with the Mobile Police Service. Samples of oral fluid were taken using the Statsure Saliva Sample (Statsure Diagnostic Systems, Framingham, MA), and the drivers’ gender, age, and nationality were recorded. Samples of oral fluid were analyzed for alcohol or drugs, for a total 28 psychoactive substances. Results: One hundred eighty-four roadside survey sessions were conducted and 10,004 drivers were asked to participate. The refusal rate was 5.8 percent. Psychoactive substances were found in 4.8 percent of the 9410 oral fluid samples analyzed. Alcohol was detected in 0.3 percent, medicinal drugs in 3.2 percent, and illegal drugs in 1.5 percent of the samples. Illegal drugs were significantly more frequently detected in samples from southeastern Norway including the capital Oslo, whereas medicinal drugs were more frequently detected in samples from southeastern Norway excluding Oslo. Illegal drugs were significantly more frequently detected in samples from drivers in urban areas than in rural areas, though there were no significant differences for alcohol and medicinal drugs. Medicinal drugs were most commonly found in samples collected during the daytime on weekdays (3.8%), and illegal drugs were most commonly found in samples collected during late night on weekdays or weekends (2.8%–3.2%). The most commonly found substances were the sleeping agent zopiclone (1.4%), the main active substance in cannabis tetrahydrocannabinol (1.1%), and the sedative drug diazepam (0.7%). The prevalence of driving with drug concentrations above the Norwegian legislative limits for blood was estimated to be about 0.2 percent for alcohol, 0.6 percent for illegal drugs, and about 1.3 percent for medicinal drugs. Conclusions: The incidence of drink driving was very low, though driving after using psychoactive illegal or medicinal drugs was more frequent. Supplemental materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention to view the supplemental file.

Modeling Incident-Related Routing Decisions by Using a Nested Logit Structure

Unusual congestion that could be caused by an incident or other traffic problems is a major source of delay for drivers in urban areas. Real-time traffic information, the building block for advanced traveler information systems (ATIS), has a promising potential for alleviating such congestion by encouraging and assisting drivers to divert to less congested routes. Traffic information is envisioned to help more informed routing decisions in case of incident-related congestion. Drivers’ routing decisions made when they are faced with such unusual congestion are investigated. The factors that influence these decisions are explored, including the effect of traffic information. A nested logit modeling structure is introduced. This model proved that the nested logit approach is superior than the simple multinomial logit in modeling the choice in cases of incident-related congestion. The model also showed that the decisions not to divert from the usual route and to divert but only around the location of the problem share unobserved terms. Familiarity and usual use of alternative routes did not affect the decision in the case of an incident. Drivers who use more than one route to work do not necessarily switch routes if they encounter unusual congestion. The nested logit model also proved the significance of traffic information, indicating a promising potential benefit of ATIS in alleviating nonrecurring congestion.

Increased risk of lung cancer among male professional drivers in urban but not rural areas of Sweden.

OBJECTIVES: To study the risk of lung cancer in different subgroups of professional drivers in urban and rural areas of Sweden. METHODS: Information on occupation and geographical region was obtained...