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"transportation systems"
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Sustainable transportation planning for a three-stage fixed charge multi-objective transportation problem
In the recent past, sustainability has become a major concern for transportation policies and planning in both developed and developing countries. This paper focuses on transportation sustainability for a three-stage fixed charge transportation problem. The major components of transportation sustainability considered include economical issues, social concerns, environmental concerns, and transportation system efficiency. Another important issue considered from a social point of view is the interrelationships between various customers of an end product, which has several benefits culminating in a healthier bottom line. The approach adopted in this paper consists of two phases, wherein the efficiency of vehicles is evaluated independently on all three parameters of sustainability using the data envelopment analysis technique in the first phase. The second phase consists of optimizing an integrated multi-objective optimization model that utilizes efficiency of the vehicles obtained from the first phase in a benefit criterion, considering the interrelationships among customers in terms of minimizing the independence values, and maximizing total profits along with many real-world constraints. Numerical illustration of a real-world case is included in order to demonstrate the utility of the proposed approach.
Journal Article
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
Sustainable optimization strategies for on-demand transportation systems: Enhancing efficiency and reducing energy use
ABSTRACT The surge in popularity of on-demand transportation services, fueled by advancements in technology and changing urban mobility patterns, has significantly reshaped urban transportation dynamics. This transformation presents challenges to traditional public transportation, raising questions about sustainability and energy efficiency. This research addresses these challenges through an explorative literature review, focusing on operational efficiency, energy transition, and policy implications. By synthesizing and analyzing existing literature, the study uncovers insights into on-demand transportation, identifies challenges and opportunities, and proposes avenues for further research. The study also develops operational and theoretical frameworks to support policy formulation and implementation in urban transportation planning, offering guidance for policymakers and urban planners. Ultimately, this research aims to contribute to developing evidence-based policies and practices that foster sustainable urban transportation networks.
Journal Article
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