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We propose a methodology for optimization of service headway and stop spacing along a low-demand bus route that minimizes operator and user costs. This study develops analytical cost models that are representative of low-demand routes by using negative binomial distribution for passenger demand for boarding and alighting pattern to estimate the probability of stopping and both random and planned arrival of passengers are considered to estimate the waiting time. Pareto optimal solutions obtained using multi-objective evolutionary algorithm, NSGA-II indicate that optimal values of headway and stop spacing are underestimated if optimized based on assumptions typical of high-demand routes which is passenger demand for boarding and alighting at bus stops randomly following a Poisson process. With the aid of the study methodology, transit planners will be able to improve the service utilization and passenger accessibility along an under-performing low demand routes by recommending minimal modifications to the existing route and bus schedule. [ABSTRACT FROM AUTHOR]

This paper presents the results of the first driver behavior questionnaire (DBQ) study on analyzing Indian drivers’ aberrant behavior for red light violations (RLVs) at signalized junctions. An ordered probit model is developed using the drivers’ socio-demographic variables. The model results indicate that age has the strongest influence on drivers’ RLV behavior, followed by driving experience and level of education. The level of perceived RLV appears to increase with higher driving frequency and two-wheeler ownership. This research is also the first to analyze the aberrant behavior differences among male and female drivers concerning their demographics and driving experiences. It is unique to note that at a certain combination of various demographics (e.g., driving experience, education level, and daily trip frequency), the men and women are becoming equal in their aberrant behavior while committing RLVs. This finding is an interesting input for developing driver training and education programs. [ABSTRACT FROM AUTHOR]

The aim of this study was to examine the relationship between initial compaction and pavement performance of a road with low-volume traffic. Data was collected from Telangana State in India. Binder was extracted from the field core samples after measuring the volumetric properties. The binder rheology was then compared with laboratory-aged VG30 bitumen. Higher air-voids in the newly constructed bituminous surface have caused binder ageing and raveling. Flaky, water-absorptive trap-aggregate worsened the problem. The fine aggregate and bitumen were stuck to the rubber-coated steel wheel in the Hamburg wheel tracking study indicating raveling. Binder reduction due to raveling was shown by slicing 30 mm field cores into two sub-layers; the bitumen content was reduced in the top slice (5.3% to 4.3%) and not in the bottom. For roads with low-volume traffic, a target density of 94% Gmm is recommended instead of the usual 92% to minimize binder ageing and raveling. [ABSTRACT FROM AUTHOR]

It is an enduring challenge to know the underlying determinants (factors and actors) for developing safe roads from the driving behavior perspective. Human (driver) errors and violations are generally referred as the most frequent cause of road accidents. This research analyzed a driver questionnaire survey of 584 drivers covering their behavior (error and deliberate violations) and safety-related attitudes while completing their driving task, at four-legged signalized intersections in India. We analyzed the driver behavior in terms of lane choice, signage and marking apprehension, tailgating, and vehicle speed consciousness at intersections, and their tendency to commit errors or violations. Driving frequency was found to have a significant effect on both Inattentive Error/Confused Error (IE/CE) and Risky Self-willed Violation (RSV). Gender was found to significantly affect only RSV and age influences only IE/CE. Highway agencies can consider the findings in developing user-friendly and self-explanatory road infrastructure with improved road users' safety features. [ABSTRACT FROM AUTHOR]

This paper contributes to the emerging literature on freight studies by identifying the optimal freight traffic analysis zone (FTAZ) system at which to conduct macro-level freight travel analysis. To arrive at the optimal scale, we develop alternate zone systems by grouping census wards with similar freight-related characteristics (industrial characteristics, commercial land use characteristics, locational characteristics and socio-demographic characteristics). The resultant zone systems are analysed at multiple geographic scales and the optimal scale of each zone system is determined by performing the Brown–Forsythe test. Results suggest that a 1:3 aggregation ratio (24–28 zones) is the optimal scale for Metropolitan FTAZs, whereas the publicly available ad-hoc zone system and prior literature on National FTAZs follow 1:10 aggregation. The study findings suggest that Metropolitan planning organizations need to reconsider their existing data collection strategy, consider a larger aggregation ratio and, by extension, adopt smaller zones to ensure that both local and global freight travel characteristics are captured in freight travel analyses. [ABSTRACT FROM AUTHOR]

Prediction of cargo volumes is important for authorities such as port administrations and port operators, as cargo throughput affects port planning and operation. The forecasting results help planners and policy-makers to take decisions on issues such as port investment, port development, berth location selection, port operation, and freight rate. The success of the port operation policy depends on the accuracy of the cargo forecast. This paper presents an improved model known as dynamic regression (DR) model to forecast cargo demand simultaneously for ports within a port system; the model is applied to forecast cargo demand at major seaports in India. Past cargo flow data from years 1980–1981 to 2013–2014 at 11 major ports in India are used for estimating the proposed model. Average prediction error is found to be within 10% for most of the ports. The DR model is used to produce forecasts up to 2019–2020 for all the ports studied. The model forecast is compared with the projections of the Ministry of Shipping, Government of India. This study is intended to provide guidance to planners in taking decisions on issues related to port infrastructure development, such as construction of new terminals and improvement of access roads to ports for the major ports in India. The study will also be beneficial to shipping agencies for their investment strategies in the Indian ports sector. [ABSTRACT FROM AUTHOR]

The study explored whether the growth of food-delivery services has affected motorcycle accident patterns. When riders check their phones for directions and orders, safety risks rise. The study conducted analysis on the spatial pattern of motorcycle accidents. Separate models for food delivery and non-food delivery motorcycle accidents (FDMAs vs. NFDMAs) were built to detect differences, and the relative importance of spatial features particular to FDMAs is discussed. Trends in motorcycle accidents in Taiwan changed from 2012 to 2019. The cross-sectional analysis revealed that hotel and restaurant density along with FDMAs reported in the previous year can predict the number of FDMAs; whereas the number of NFDMAs can be determined by population density and the number of 3-legged intersections. The spatial analysis of motorcycle accidents help increase couriers' risk awareness in specific workplaces, provide insurance companies with a benchmark for regional pricing differentiation, and offer support for distracted riding regulations. [ABSTRACT FROM AUTHOR]