Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
95,398
result(s) for
"Transportation systems"
Sort by:
The Safety of Intelligent Driver Support Systems
Road telematics and driver assistance systems offer a real opportunity to aid mobility and road safety. However, they also raise numerous questions. Problems related to the design and evaluation of intelligent driver support systems (IDSSs) and social perspectives related to their large scale introduction may only be fully addressed from a multi-disciplinary viewpoint. People from both engineering and social sciences, should be involved and this book provides such knowledge from both a human and social factors perspective.
eBook
An efficient algorithm for optimal route node sensing in smart tourism Urban traffic based on priority constraints
The public transportation system is now dealing with a number of problems brought on by the sharp increase in automobile ownership in cities as well as the buildup of vehicles as a result of events and accidents. However, the city’s limited road network capacity cannot keep up with the increasing traffic demand, which further worsens travel conditions and results in a waste of time and money. Given that it is challenging to enhance the capacity of the road network in practice, efficient vehicle travel and evacuation using algorithms has emerged as a recent study focus. It is crucial to learn how to manage urban traffic issues during emergencies and maintain smooth and safe traffic flow. The existing studies only consider the optimized route selection for individual vehicles, signal cycle of traffic lights and deploy historical data to disperse the vehicles on alternative routes. However, such works do not consider the conflict of routes between vehicles, the customized traffic demand of each vehicle and uncertain traffic conditions. Therefore, this paper proposes a novel approach to facilitate the user to select the optimal route with real-time traffic scenario. Furthermore, the Nash equilibrium is established by mutual information swapping and self-adaptive learning method. Simulation results show that the proposed algorithm has better route selection capability in real-time personalized road traffic as compared with existing algorithms.
Journal Article
Passenger flow prediction in bus transportation system using deep learning
The forecasting of bus passenger flow is important to the bus transit system’s operation. Because of the complicated structure of the bus operation system, it’s difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.
Journal Article
Governance of the Smart Mobility Transition
The transition towards 'smarter' autonomous transport systems calls for a rethink in how transport is governed/who governs it, to ensure a step-change to a more sustainable future. This book critically reflects on these governance challenges analysing the role of the state; the new actors and discourses; and the implications for state capacity.
eBook
Exploring spatial heterogeneity in the impact of built environment on taxi ridership using multiscale geographically weighted regression
Due to its flexibility and door-to-door service, taxis are an integral part of the urban transportation system. They have become an essential solution to the first/last mile problem. Even though much research has been conducted on the effects of built environment variables on taxi passengers’ travel behaviors, few have accounted for the spatial heterogeneity embedded in multiscale spatial processes. This study applies multiscale geographically weighted regression (MGWR) to investigate the associations between taxi ridership and spatial contexts to address the gaps. The MGWR considerably improves modeling fit compared to the global OLS model by capturing the spatially varying processes at different scales. The results demonstrate the existence of strong spatial non-stationarity in the various built environment factors affecting the spatial distribution of taxi pick-ups and drop-offs. Specifically, increased residential density induces more taxi demand in areas with less access to public transportation than their surrounding units. Increasing bus coverage where bus coverage is relatively low may attract more commuters to adopt taxi plus bus mode for commuting. Road network density has a more substantial effect on taxi ridership in the south end of the city than in the north. The former is characterized by lower road density. This study reveals the complex relationships between the built environment and the distribution of taxi ridership at different spatial scales and provides valuable insights for transport planning, taxi resource allocation and urban governance.
Journal Article