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Meet the bus driver = Te presento a los conductores de autobús
Easy-to-follow text, presented in both English and standard Latin American Spanish, introduces beginning readers to a variety of bus drivers, and a helpful picture glossary is included to strengthen vocabulary skills.
Book
Costs and Benefits of Electrifying and Automating Bus Transit Fleets
Diesel-powered, human-driven buses currently dominate public transit options in most U.S. cities, yet they produce health, environmental, and cost concerns. Emerging technologies may improve fleet operations by cost-effectively reducing emissions. This study analyzes both battery-electric buses and self-driving (autonomous) buses from both cost and qualitative perspectives, using the Capital Metropolitan Transportation Authority’s bus fleet in Austin, Texas. The study predicts battery-electric buses, including the required charging infrastructure, will become lifecycle cost-competitive in or before the year 2030 at existing U.S. fuel prices ( $2.00/gallon), with the specific year depending on the actual rate of cost decline and the diesel bus purchase prices. Rising diesel prices would result in immediate cost savings before reaching $ 3.30 per gallon. Self-driving buses will reduce or eliminate the need for human drivers, one of the highest current operating costs of transit agencies. Finally, this study develops adoption schedules for these technologies. Recognizing bus lifespans and driver contracts, and assuming battery-electric bus adoption beginning in year-2020, cumulative break-even (neglecting extrinsic benefits, such as respiratory health) occurs somewhere between 2030 and 2037 depending on the rate of battery cost decline and diesel-bus purchase prices. This range changes to 2028 if self-driving technology is available for simultaneous adoption on new electric bus purchases beginning in 2020. The results inform fleet operators and manufacturers of the budgetary implications of converting a bus fleet to electric power, and what cost parameters allow electric buses to provide budgetary benefits over their diesel counterparts.
Journal Article
A bottom-up clustering approach to identify bus driving patterns and to develop bus driving cycles for Hong Kong
Bus transport has been an important mode taking up a significant share of urban travel demand and thus the corresponding impacts on the environment are of great concerns. Use of driving cycles to evaluate the environmental impacts of buses has attracted much attention in recent years worldwide. The franchised bus service is currently playing important roles in the public transport system in Hong Kong; however, there is no driving cycle developed specifically for them. A set of bus driving cycle was therefore developed using a bottom-up approach where driving data on the bus network with mixed characteristics were collected. Using the Ward’s method for clustering, the collected data were then categorized into three clusters representing distinct franchised bus route patterns in Hong Kong. Driving cycles were then developed for each route pattern including (i) congested urban routes with closely spaced bus stops and traffic junctions; (ii) inter-district routes containing a number of stop-and-go activities and a significant portion of smoother high speed driving; and (iii) early morning express routes and mid-night routes connecting remote residential areas and urban areas. These cycles highlighted the unique low-speed and aggressive driving characteristics of bus transport in Hong Kong with frequent stop-and-go activities. The findings from this study would definitely be helpful in assessing the exhaust emissions, fuel consumptions as well as energy consumptions of bus transport. The bottom-up clustering approach adopted in this study would also be useful in identifying specific driving patterns based on vehicle speed trip data with mixed driving characteristics. It is believed that this approach is especially suitable for assessing fixed route public transport modes with mixed driving characteristics.
Journal Article
Comparisons of Driving Characteristics between Electric and Diesel-Powered Bus Operations along Identical Bus Routes
The energy consumption profiles of conventional fuelled and electric vehicles are different due to the fundamental differences in the driving characteristics of these vehicles, which have been actively researched elsewhere but mostly on the basis of uncommon geographical contexts. This study, therefore, collected driving data on electric and conventional diesel buses running along exactly the same set of bus routes in Hong Kong during normal daily revenue operations. This enabled a fair comparison of driving characteristics for both types of bus under identical real-life, on-road driving conditions, which highlighted the originality and contributions of this study. A three-step approach was adopted to carry out detailed driving pattern analyses, which included key driving parameters, speed–acceleration probability distributions (SAPDs), and vehicle-specific power (VSP) distributions. Results found that route-based comparisons did highlight important differences in driving patterns between electric and diesel buses that might have been smoothed out by analyses with mixed-route datasets. In particular, the spread, intensity, and directions of these differences were found to be exaggerated at the route-based level. The differences in driving patterns varied across different routes, which has significant implications on vehicle energy consumption. Government agencies and/or bus operators should make references to these results in formulating electric bus deployment plans.
Journal Article
Coordination Optimization of Real-Time Signal Priority of Self-Driving Buses at Arterial Intersections Considering Private Vehicles
Transit Signal Priority (TSP) is a system designed to grant right-of-way to buses, yet it can lead to delays for private vehicles. With the rapid advancement of network technology, self-driving buses have the capability to efficiently acquire road information and optimize the coordination between vehicle arrival and signal timing. However, the complexity of arterial intersections poses challenges for conventional algorithms and models in adapting to real-time signal priority. In this paper, a novel real-time signal-priority optimization method is proposed for self-driving buses based on the CACC model and the powerful deep Q-network (DQN) algorithm. The proposed method leverages the DQN algorithm to facilitate rapid data collection, analysis, and feedback in self-driving scenarios. Based on the arrival states of both the bus and private vehicles, appropriate actions are chosen to adjust the current-phase green time or switch to the next phase while calculating the duration of the green light. In order to optimize traffic balance, the reward function incorporates an equalization reward term. Through simulation analysis using the SUMO framework with self-driving buses in Zhengzhou, the results demonstrate that the DQN-controlled self-driving TSP optimization method reduces intersection delay by 27.77% and 30.55% compared to scenarios without TSP and with traditional active transit signal priority (ATSP), respectively. Furthermore, the queue length is reduced by 33.41% and 38.21% compared to scenarios without TSP and with traditional ATSP, respectively. These findings highlight the superior control effectiveness of the proposed method, particularly during peak hours and in high-traffic volume scenarios.
Journal Article
Toward a Comfortable Driving Experience for a Self-Driving Shuttle Bus
The convergence of mechanical, electrical, and advanced ICT technologies, driven by artificial intelligence and 5G vehicle-to-everything (5G-V2X) connectivity, will help to develop high-performance autonomous driving vehicles and services that are usable and convenient for self-driving passengers. Despite widespread research on self-driving, user acceptance remains an essential part of successful market penetration; this forms the motivation behind studies on human factors associated with autonomous shuttle services. We address this by providing a comfortable driving experience while not compromising safety. We focus on the accelerations and jerks of vehicles to reduce the risk of motion sickness and to improve the driving experience for passengers. Furthermore, this study proposes a time-optimal velocity planning method for guaranteeing comfort criteria when an explicit reference path is given. The overall controller and planning method were verified using real-time, software-in-the-loop (SIL) environments for a real-time vehicle dynamics simulation; the performance was then compared with a typical planning approach. The proposed optimized planning shows a relatively better performance and enables a comfortable passenger experience in a self-driving shuttle bus according to the recommended criteria.
Journal Article
Real-time application of Pontryagin’s Minimum Principle to fuel cell hybrid buses based on driving characteristics of buses
The Pontryagin’s Minimum Principle (PMP)-based energy management strategy is regarded as one of the most promising strategies for hybrid vehicles, given that it instantaneously provides optimal power distribution solutions between power sources. The real-time application of the PMP, however, is still difficult due to the heavy computational burden and the uncertainty of the future vehicle driving cycle. The driving characteristics of city buses, including the bus dwell time at bus stops and comparatively specified driving routes, are very helpful when realizing the PMP to hybrid powertrains. An energy management approach of fuel cell hybrid buses for real-time applications is proposed in this research based on the driving characteristics of buses, in which a reference driving cycle (RDC) is defined for a bus driving route and the bus dwell time is sufficiently used to calculate the PMP-based power distribution solutions. In order to reflect the deviation of the real bus driving route from the RDC, the control parameter of the PMP is updated at every bus stop before calculating the solutions. Simulation results show that the power distribution result of the proposed energy management approach reaches that of the offline PMP application and the discrepancy is within 2.65% for the driving cycles studied.
Journal Article
Detecting Anomalous Bus-Driving Behaviors from Trajectories
In urban transit systems, discovering anomalous bus-driving behaviors in time is an important technique for monitoring the safety risk of public transportation and improving the satisfaction of passengers. This paper proposes a two-phase approach named Cygnus to detect anomalous driving behaviors from bus trajectories, which utilizes collected sensor data of smart phones as well as subjective assessments from bus passengers by crowd sensing. By optimizing support vector machines, Cygnus discovers the anomalous bus trajectory candidates in the first phase, and distinguishes real anomalies from the candidates, as well as identifies the types of driving anomalies in the second phase. To improve the anomaly detection performance and robustness, Cygnus introduces virtual labels of trajectories and proposes a correntropy-based policy to improve the robustness to noise, combines the unsupervised anomaly detection and supervised classification, and further refines the classification procedure, thus forming an integrated and practical solution. Extensive experiments are conducted on real-world bus trajectories. The experimental results demonstrate that Cygnus detects anomalous bus-driving behaviors in an effective, robust, and timely manner.
Journal Article
ENERGY ANALYSIS OF PUBLIC TRANSPORT BUSES OF MEXICO CITY
Surface transportation in Mexico City contributes to pollutant gas emissions, favoring the occurrence of respiratory diseases in the inhabitants while also causing the increase of greenhouse gases in the atmosphere. This study evaluates the use of a bus fleet powered by fuel cells, for sustainable development of public transport in the city. Three types of buses in four main routes of public transport in Mexico City are compared to determine their characteristics of required power and energy, fuel consumption and pollutant emissions. Simulations are performed using driving cycles, technical specifications of buses based on real data collected by bus operators, technical specifications and technical reports of the city's government. Results show that the Insurgentes and Eje Central routes have the highest fuel consumption and thus emit a higher concentration of toxic gases, compared to the other routes. It is also concluded that there are numerous operational, environmental and economic benefits of fuel cell electric buses (FCEB) over traditional diesel or diesel hybrid buses. The most important benefit would be the reduction of fuel consumption and, hence, gas emissions by 37 % over the bus with diesel engine and 30 % compared to diesel hybrid bus. If hydrogen cell buses were implemented, Insurgentes and Tepalcates routes would have a hydrogen consumption of 14.4kg /100km, while Tláhuac and Eje Central one of 9kg/100km. Finally, the main difficulties for implementing the FCEB are analyzed.
Journal Article