Search results

The purpose of this study is to investigate the influence of institutional dynamics on road accidents and whether this relationship is moderated by information and communication technology (ICT).

The study adopted a quantitative approach with 133 respondents. Research hypotheses were tested in AMOS version 21. In addition, moderated regression analysis was used to test the moderating role of ICT on the relationship between institutional dynamics and road accidents.

The results show that vehicle maintenance, policy enforcement, safety culture, driver training and driver management positively influence road accidents. Moreover, the study established that ICT moderates the relationship between institutional dynamics and road accidents.

The results of this study serve as a practical guideline for policymakers in the road haulage sector. Managers may gain insights on how to design effective interventions to reduce road accidents.

This research contributes to the existing body of knowledge by exploring previously unexplored moderating paths in the relationship between institutional dynamics and road accidents. By highlighting the moderating role of ICT, the study sheds new light on the institutional dynamics that influence road accidents in the context of road haulage companies.

A considerable amount of police evaluation research focuses on innovative approaches to reduce crime at places. This is hardly coincidental; policing and place-based scholars have found crime is highly concentrated, and when police focus on these places, they can prevent and reduce crime. The regularity of such findings led Weisburd (2015) to assert the existence of a “law of crime concentration.” Given that bold assertion, the authors test whether the law of crime concentration is generalizable to one of the most common public safety concerns that police handle—traffic crashes.

To determine whether the law of crime concentration applies to traffic crashes, the authors examined crash locations and times in all counties in Utah across four years. Following and expanding on Weisburd's methods, the authors calculate the bandwidth of concentration for these crashes and analyze various types by severity and possible explanations for variations in crash concentrations across the state.

A small proportion of street segments and intersections experience a disproportionately high number of crashes, and the degree of concentration of crashes may be even higher than that of crime. Further, there are variations in the levels of crash concentration across counties and in the severity of injuries resulting from the crashes.

Place-based criminologists and policing scholars have not often explored traffic crashes in their analyses. Yet, traffic problems take up a significant amount of law enforcement time and resources and are often priorities for most law enforcement agencies. Given what the authors know from traffic, policing and crime and place research, targeted approaches at micro traffic crash hot spots can be beneficial for public safety prevention.

This study is the first to explore the application of Weisburd's Law of Crime Concentration to traffic crashes. Given that police spend a significant amount of time and resources on traffic-related problems in their jurisdiction, finding more effective, evidence-based approaches to address this public safety concern should be a high priority for police and researchers alike.

The purpose of this study was to explore how transport companies in Uganda execute strategies in a turbulent business environment.

The study adopted an exploratory qualitative methodology using the data collected through an open-ended instrument. Utilizing the qualitative data analysis software QSR NVivo9, the data were analyzed following the Gioia's methodology. Verbatim texts were used to explain the emergent themes.

The study's findings show that to successfully execute strategies, companies in Uganda communicate, coordinate and put control systems in their operations. The activities undertaken include customer care, timely settlement of complaints, comfortable seats, playing local music, partnerships with reliable fuel stations, setting up strategic offices, cost management, use of experienced drivers, sub-renting vehicles and inspections.

The study produces a pioneering result of how transport companies execute strategies in a turbulent business environment, an aspect that has not been adequately highlighted in previous studies.

The purpose of this paper is to develop a proof-of-concept (POC) Forward Collision Warning (FWC) system for the motorcyclist, which determines a potential clash based on time-to-collision and trajectory of both the detected and ego vehicle (motorcycle).

This comes in three approaches. First, time-to-collision value is to be calculated based on low-cost camera video input. Second, the trajectory of the detected vehicle is predicted based on video data in the 2 D pixel coordinate. Third, the trajectory of the ego vehicle is predicted via the lean direction of the motorcycle from a low-cost inertial measurement unit sensor.

This encompasses a comprehensive Advanced FWC system which is an amalgamation of the three approaches mentioned above. First, to predict time-to-collision, nested Kalman filter and vehicle detection is used to convert image pixel matrix to relative distance, velocity and time-to-collision data. Next, for trajectory prediction of detected vehicles, a few algorithms were compared, and it was found that long short-term memory performs the best on the data set. The last finding is that to determine the leaning direction of the ego vehicle, it is better to use lean angle measurement compared to riding pattern classification.

The value of this paper is that it provides a POC FWC system that considers time-to-collision and trajectory of both detected and ego vehicle (motorcycle).

Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.

The model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).

Evaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.

Presents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

Representations of time are commonly used to construct narratives in visualisations of data. However, since time is a value-laden concept, and no representation can provide a full, objective account of “temporal reality”, they are also biased and political: reproducing and reinforcing certain views and values at the expense of alternative ones. This conceptual paper aims to explore expressions of temporal bias and politics in data visualisation, along with possibly mitigating user approaches and design strategies.

This study presents a theoretical framework rooted in a sociotechnical view of representations as biased and political, combined with perspectives from critical literacy, radical literacy and critical design. The framework provides a basis for discussion of various types and effects of temporal bias in visualisation. Empirical examples from previous research and public resources illustrate the arguments.

Four types of political effects of temporal bias in visualisations are presented, expressed as limitation of view, disregard of variation, oppression of social groups and misrepresentation of topic and suggest that appropriate critical and radical literacy approaches require users and designers to critique, contextualise, counter and cross beyond expressions of the same. Supporting critical design strategies involve the inclusion of multiple datasets and representations; broad access to flexible tools; and inclusive participation of marginalised groups.

The paper draws attention to a vital, yet little researched problem of temporal representation in visualisations of data. It offers a pioneering bridging of critical literacy, radical literacy and critical design and emphasises mutual rather than contradictory interests of the empirical sciences and humanities.

To support the standardized evaluation of bicyclist automatic emergency braking (AEB) systems, test scenarios, test procedures and test system hardware and software tools have been investigated and developed by the Transportation Active Safety Institute (TASI) at Indiana University-Purdue University Indianapolis. This paper aims to focus on the development of test scenarios and bicyclist surrogate for evaluating vehicle–bicyclist AEB systems.

The harmonized general estimates system (GES)/FARS 2010-2011 crash data and TASI 110-car naturalistic driving data (NDD) are used to determine the crash geometries and environmental factors of crash scenarios including lighting conditions, vehicle speeds, bicyclist speeds, etc. A surrogate bicyclist including a bicycle rider and a bicycle surrogate is designed to match the visual and radar characteristics of bicyclists in the USA. A bicycle target is designed with both leg pedaling and wheel rotation to produce proper micro-Doppler features and generate realistic motion for camera-based AEB systems.

Based on the analysis of the harmonized GES/FARS crash data, five crash scenarios are recommended for performance testing of bicyclist AEB systems. Combined with TASI 110-car naturalistic driving data, the crash environmental factors including lighting conditions, obscuring objects, vehicle speed and bicyclist speed are determined. The surrogate bicyclist was designed to represent the visual and radar characteristics of the real bicyclists in the USA. The height of the bicycle rider mannequin is 173 cm, representing the weighted height of 50th percentile US male and female adults. The size and shape of the surrogate bicycle were determined as 26-inch wheel and mountain/road bicycle frame, respectively. Both leg pedaling motion and wheel rotation are suggested to produce proper micro-Doppler features and support the camera-based AEB systems.

The results have demonstrated that the developed scenarios, test procedures and bicyclist surrogate will provide effective objective methods and necessary hardware and software tools for the evaluation and validation of bicyclist AEB systems. This is crucial for the development of advanced driver assistance systems.

Railway-level crossings (RLCs) are the point of intersection between rail and road users and are therefore hotpots of road-rail user conflict and catastrophic collisions. The purpose of this study is to assess the risks associated with RLCs and suggest probable reduction measures. Through questionnaires and visual inspection, the authors identify the safety risks, hazards and hazardous events at some railway crossing of Addis Ababa light rail transit (AA-LRT) north-south (N-S) route. The identified risky events are then categorized based on As Low As Reasonably Practicable (ALARP) principle and generic risk ranking matrix. The authors then examine existing safety management measures at railway crossing and assess the need for additional safety management. Five major crossings on the 16.9 km (10.5 mi) N-S line, starting from Menelik II Square to Kality, were considered for the study. This study is carried out by data collection from about 145 stakeholders and the application of statistical data and risk analysis methods. The major findings of this study and the recommendations for improvement are suggested.

The research followed a case study approach. Through questionnaires and visual inspection, the authors identify the safety risks, hazards and hazardous events at some railway crossing of AA-LRT N-S route. The identified risky events are then categorized based on ALARP principle and generic risk ranking matrix. Collected data was then analyzed using SPSS to deduce relationships.

The study findings reveal human factors as the greatest cause of accidents, injury or death. About 22% of hazards identified by category are human factors, whereas 20% are because of technical problems. Intolerable risks stand at 42%, whereas the tolerable risks are at 36% according to risk classification results as per the ALARP model. Because the process of risk management is a long-term cycle, its importance should not be missed at any time.

Because of design considerations of RLCs and the difference in generalized human behaviors for people of a given region, the results are limited to AA-LRT RLCs. This study opens a discourse for detailed evaluations, qualitative and quantitative analysis into the categorized identified hazards. There is also room for additional research into the performance of RLCs aimed at formulating standard necessary features that should be included on RLCs for proper risk control especially in emerging economies.

The research paper is original and has not been submitted for consideration to other journals.

The purpose of this paper is to perform fine classification of road traffic visibility based on the characteristics of driving behavior under different visibility conditions.

A driving simulator experiment was conducted to collect data of speed and lane position. ANOVA was used to explore the difference in driving behavior under different visibility conditions.

The results show that only average speed is significantly different under different visibility conditions. With the visibility reducing, the average vehicle speed decreases. The road visibility conditions in a straight segment can be divided into five levels: less than 20, 20-30, 35-60, 60-140 and more than 140 m. The road visibility conditions in a curve segment can be also divided into four levels: less than 20, 20-30, 35-60 and more than 60 m.

A fine classification of road traffic visibility has been performed, and these classifications help to establish more accurate control measures to ensure road traffic safety under low-visibility conditions.