Optimal timetable development for community shuttle network with metro stations

This paper investigates an issue for optimizing synchronized timetable for community shuttles linked with metro service. Considering a passenger arrival distribution, the problem is formulated to optimize timetables for multiple community shuttle routes, with the objective of minimizing passenger’s schedule delay cost and transfer cost. Two constraints, i.e., vehicle capacity and fleet size, are modeled in this paper. The first constraint is treated as soft, and the latter one is handled by a proposed timetable generating method. Two algorithms are employed to solve the problem, i.e., a genetic algorithm (GA) and a Frank–Wolfe algorithm combined with a heuristic algorithm of shifting departure times (FW-SDT). FW-SDT is an algorithm specially designed for this problem. The simulated and real-life examples confirm the feasibility of the two algorithms, and demonstrate that FW-SDT outperforms GA in both accuracy and effectiveness.     

一种基于拓展支持向量机的无人船路径规划方法

为有效解决无人船实现智能航行面临的路径规划问题，提出一种基于拓展支持向量机（SVM）的路径规划方法。该方法在传统SVM路径规划方法的基础上，考虑任务途中障碍物的规避风险，将工作任务的起止点作为约束条件对传统方法进行拓展，并针对不同的应用情景给出线性路径规划和非线性路径规划的求解方法。同时，为提高路径规划的效率，从SVM的原理出发，提出一种训练集裁剪的方法，大幅度减少利用SVM求解路径时所需的训练样本个数。此外，通过仿真验证该方法的可行性和有效性。     

Road traffic conditions in Kenya: Exploring the policies and traffic cultures from unstructured user-generated data using NLP

Road traffic accidents (RTA) are a prevalent cause of fatality with African countries having the highest fatality index (25–34 per quota). The World Health Organization estimates Kenya's fatality rate due to RTA at 28 per quota. From literature, the country's fatality and injuries have increased by 26% and 46.5%, respectively, since the year 2015. The country is faced with incomplete RTA data capturing, hindering effective planning and policy adjustments to curb the menace. In this paper, we scrapped user-generated data (Twitter) and national transport and safety authority's (NTSA) reports to shed light on traffic safety, practices, and cultures in the country. To this end, we gathered 1,000,000 tweets and 8000 speeding entries between 2015 and 2021 and performed natural language processing (NLP) and quantitative study of the data. We applied NLP and n-gram search of keywords to categorize data into 8 topics: traffic, public service vehicle (PSVs), policing, accident, infrastructure, recklessness, robbery, and corruption. From the data, policing, which touches on all police and law-enforcement-related activity was found to be highly correlated with PSVs, recklessness, accidents, traffic congestion, robbery, infrastructure, and corruption with indices of r(76) = 0.92, 0. 91, 0.87, 0.82, 0.81, 0.76, and 0.70, respectively with p < 0.001. The topic modeling confirmed the identified topics to be the latent discussion issues affecting the public. From the study, PSVs, policing and traffic flow were isolated as key issues that ought to be addressed immediately. The research recommended the integration of driver monitoring systems to strengthen policing. The research, which utilized unstructured data, points to the utility of data mining which would greatly benefit traffic research, particularly African-based studies, that suffer from data inadequacy.     

Estimating potential increases in travel with autonomous vehicles for the non-driving,elderly and people with travel-restrictive medical conditions

Automated vehicles represent a technology that promises to increase mobility for many groups, including the senior population (those over age 65) but also for non-drivers and people with medical conditions. This paper estimates bounds on the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from the non-driving and senior populations and people with travel-restrictive medical conditions. In addition, these bounding estimates indicate which of these demographics could have the greatest increases in annual vehicle miles traveled (VMT) and highlight those age groups and genders within these populations that could contribute the most to the VMT increases. The data source is the 2009 National Household Transportation Survey (NHTS), which provides information on travel characteristics of the U.S. population. The changes to light-duty VMT are estimated by creating and examining three possible travel demand wedges. In demand wedge one, non-drivers are assumed to travel as much as the drivers within each age group and gender. Demand wedge two assumes that the driving elderly (those over age 65) without medical conditions will travel as much as a younger population within each gender. Demand wedge three makes the assumption that working age adult drivers (19–64) with medical conditions will travel as much as working age adults without medical conditions within each gender, while the driving elderly with medical any travel-restrictive conditions will travel as much as a younger demographic within each gender in a fully automated vehicle environment. The combination of the results from all three demand wedges represents an upper bound of 295 billion miles or a 14% increase in annual light-duty VMT for the US population 19 and older. Since traveling has other costs besides driving effort, these estimates serve to bound the potential increase from these populations to inform the scope of the challenges, rather than forecast specific VMT scenarios.     

Accounting for traffic speed dynamics when calculating COPERT and PHEM pollutant emissions at the urban scale

Coupling a traffic microsimulation with an emission model is a means of assessing fuel consumptions and pollutant emissions at the urban scale. Dealing with congested states requires the efficient capture of traffic dynamics and their conditioning for the emission model. Two emission models are investigated here: COPERT IV and PHEM v11. Emission calculations were performed at road segments over 6 min periods for an area of Paris covering 3 km². The resulting network fuel consumption (FC) and nitrogen oxide (NOx) emissions are then compared. This article investigates: (i) the sensitivity of COPERT to the mean speed definition, and (ii) how COPERT emission functions can be adapted to cope with vehicle dynamics related to congestion. In addition, emissions are evaluated using detailed traffic output (vehicle trajectories) paired with the instantaneous emission model, PHEM.COPERT emissions are very sensitive to mean speed definition. Using a degraded speed definition leads to an underestimation ranging from −13% to −25% for fuel consumption during congested periods (from −17% to −36% respectively for NOx emissions). Including speed distribution with COPERT leads to higher emissions, especially under congested conditions (+13% for FC and +16% for NOx). Finally, both these implementations are compared to the instantaneous modeling chain results. Performance indicators are introduced to quantify the sensitivity of the coupling to traffic dynamics. Using speed distributions, performance indicators are more or less doubled compared to traditional implementation, but remain lower than when relying on trajectories paired with the PHEM emission model.     

Evaluation of eco-driving training for vehicle fuel use and emission reduction: A case study in Australia

This study evaluates effectiveness of driver education teaching greater fuel efficiency (Eco-Driving) in a real world setting in Australia. The driving behaviour, measured in fuel use (litres per 100 km of travel) of a sample of 1056 private drivers was monitored over seven months. 853 drivers received education in eco-driving techniques and 203 were monitored as a control group. A simple experimental design was applied comparing the pre and post training fuel use of the treated sample compared to a control sample. This study found the driver education led to a statistically significant reduction in fuel use of 4.6% or 0.51 litres per 100 km compared to the control group.     

Soft Radial Basis Cellular Neural Network (SRB-CNN) based robust low-cost truck detection using a single presence detection sensor

This paper does present a comprehensive concept for a robust and reliable truck detection involving solely one single presence sensor (e.g. an inductive loop, but also any other presence sensor) at a signalized traffic junction. Hereby, two operations modes are distinguished: (a) during green traffic light phases, and (b) a much challenging case, during red traffic light phases. First, it is shown how difficult the underlying classification task is, this mainly due to strongly overlapped classes, which cannot be easily separated by simple hyper-planes. Then, a novel soft radial basis cellular neural/nonlinear network (SRB-CNN) based concept is developed, validated and extensively benchmarked with a selection of the best representatives of the current related state-of-the-art classification concepts (namely the following: support vector machines with radial basis function, artificial neural network, naive Bayes, and decision trees). For benchmarking purposes, all selected competing classifiers do use the same features and the superiority of the novel CNN based classifier is thereby underscored, as it strongly outperforms the other ones. This novel SRB-CNN based concept does satisfactorily fulfill the hard industrial requirements regarding robustness, low-cost, high processing speed, low memory consumption, and the capability to be deployed in low cost embedded systems.     

Analysis of fuel consumption and pollutant emissions of regulated and alternative driving cycles based on real-world measurements

Discrepancies between real-world use of vehicles and certification cycles are a known issue. This paper presents an analysis of vehicle fuel consumption and pollutant emissions of the European certification cycle (NEDC) and the proposed worldwide harmonized light vehicles test procedure (WLTP) Class 3 cycle using data collected on-road. Sixteen light duty vehicles equipped with different propulsion technologies (spark-ignition engine, compression-ignition engine, parallel hybrid and full hybrid) were monitored using a portable emission measurement system under real-world driving conditions. The on-road data obtained, combined with the Vehicle Specific Power (VSP) methodology, was used to recreate the dynamic conditions of the NEDC and WLTP Class 3 cycle. Individual vehicle certification values of fuel consumption, CO₂, HC and NO_x emissions were compared with test cycle estimates based on road measurements. The fuel consumption calculated from on-road data is, on average, 23.9% and 16.3% higher than certification values for the recreated NEDC and WLTP Class 3 cycle, respectively. Estimated HC emissions are lower in gasoline and hybrid vehicles than certification values. Diesel vehicles present higher estimated NO_x emissions compared to current certification values (322% and 326% higher for NO_x and 244% and 247% higher for HC + NO_x for NEDC and WLTP Class 3 cycle, respectively).     

Estimation of the response of a deeply immersed cylinder to the shock wave generated by an underwater explosion

The work presented in this paper is focused on the development of a simplified method to study the structural response of a deeply immersed cylinder subjected to the primary shock wave generated by an underwater explosion. The proposed analytical model is based on the string-on-foundation method initially developed by Hoo Fatt and Wierzbicki, who converted the two dimensional boundary value problem of a cylindrical shell to an equivalent one-dimensional problem of a plastic string on a plastic foundation. This method has already been extended by the authors to study the shock wave response of an unstiffened cylinder immersed in shallow water. The present work focuses on deep-immersed cylinders subjected to both high hydrostatic pressure and explosion shock wave. The elastic deformation energy of the cylinder under hydrostatic pressure is first calculated and used to determine the initial conditions of the dynamic problem. Cylinder deflection and plastic deformation energy are then calculated for various immersion depths. When confronted to numerical results, the proposed model appears to underestimate the increase of deflection and absorbed energy with the immersion depth. A thorough analysis of the results post-processed from Ls-Dyna/USA finite element simulations highlights a new mechanism which is due to the action of hydrostatic pressure that continues to push inward the immersed cylinder. In order to improve the analytical model, a correction factor on the hydrostatic pressure is introduced but it is finally concluded that a new mechanism dedicated to the late action of the hydrostatic pressure still needs to be developed.