Impact Of Intelligent Transport Systems Research Articles

Impacts of intelligent transportation systems on energy conservation and emission reduction of transport systems: A comprehensive review

With the development of smart cities, new requirements have been put forward for the control of carbon emissions (CEs) in the transportation system. Intelligent transportation systems (ITS) provide a solution to the problems of traffic congestion and CEs caused by the rapid increase in the number of vehicles. This work aims at the impact of ITS on the energy conservation and emission reduction (ECER) of transportation networks. This work screened more than 100 pieces of relevant literature on ECER from Google Scholar and classified and analyzed them one by one. First, the development of the transportation system is studied. The monitoring of the transportation network is developing in a brilliant direction, from the initial induction coil detector to the current ITS. Then, the realization path of the ECER transportation system is studied from three aspects: transportation, transportation organization and management, and energy upgrading and replacement. Finally, the impact of ITS on ECER in different transportation domains is summarized. The results indicate that the traffic system has undergone traditional video surveillance and has become increasingly intelligent. Visually presenting traffic data in the management system plays a very influential role in alleviating traffic congestion and vehicle ECER. However, selecting the best vehicle parameters is necessary to realize ECER for the transport system. At the same time, the implementation of these energy-saving measures requires the traction and promotion of government policies in order for ECER transportation to fulfill its potential role. • Study the impact of ITS on Energy Conservation and Emission Reduction (ECER) of the transportation networks. • The realization path of the transportation system ECER is studied from three aspects. • The impact of ITS on ECER in different transportation fields is summarized.

A Microscopic Model of Vehicle CO₂ Emissions Based on Deep Learning—A Spatiotemporal Analysis of Taxicabs in Wuhan, China

It is important to assess environmental impact of intelligent transportation systems, and hence developing a vehicle emission model with high accuracy has been a long-standing topic in transportation research. However, current vehicle emission models are either overly simple using average speed, resulting in low estimation accuracy, or they are too complicated requiring excessive inputs, relying on too much prior knowledge. In this study, we develop and evaluate a deep learning-based vehicle emission model (DL-VEM) to estimate the instantaneous CO2 emissions of taxicabs. First, we examine the correlation between observed emissions and vehicle driving condition data collected in a PEMS experiment. Then, an end-to-end deep learning structure is developed to model patterns of vehicle emissions. Specifically, LSTM networks are used to learn temporal dependencies of historical driving patterns, and fully connected networks are employed to extract deep features of current driving behaviors and external environment. Our model aggregates the outputs of these networks using different learnable weights. Experiments were conducted in Wuhan, China, where our model was trained and validated using observed datasets. Compared with the state-of-the-art models, our model achieved higher accuracy in estimating CO2 emissions. Thereafter, it was applied to a taxicab trajectory dataset in one day, and spatiotemporal patterns of CO2 emissions were presented using different fuel types. Importantly, we find that an increment of 24.94% emissions can be expected if petrol instead of compressed natural gas was used by each taxicab in Wuhan.

Impact of Intelligent Transportation Systems on Road Traffic Safety

Impact of Intelligent Transportation Systems on Road Traffic Safety

The impact of Intelligent Transport Systems to reduce congestion in cities

The dynamic development of urban areas has created the need to adapt computer systems used to increased passenger traffic. Increasingly common phenomenon of congestion, contributed to intensify efforts to improve the efficiency of the control flows in agglomerations. For this purpose cities begin to use more and more modern-solving software to improve the functioning of public transport. The aim of the article is to assess and review of current solutions that can be successfully implemented in communication systems, cities in Poland. The publication is also intended to determine the prospects for the development of intelligent transport systems in the future as well as the effects resulting from their continued use.

Benefits and challenges of multi‐driver simulator studies

Lessons learned from a multi-driver simulator study are presented. The motivation of the use of a multi-driver simulator is outlined by describing the need to study driver–driver interaction and the so far existing insufficient methods. Therefore the multi-driver simulator MoSAIC ‘Modular and Scalable Application-platform for Intelligent Transport Systems (ITS) components’ at the German Aerospace Center will be presented. Its benefits and requirements in the conduction and analysis of obtained data are derived from theoretical analysis of recent work on multi-driver simulators as well as methodological requirements to study driver behaviour. Based on a study within the German national project UR:BAN (German acronym ‘Urbaner Raum: Benutzergerechte Assistenzsysteme und Netzmanagement’) to evaluate the impact of cooperative ITS on non-equipped drivers, further lessons learned are collected. Driving parameters are operationalised to evaluate the safety criticality as well as the overall acceptance of the equipped driver's behaviour from the perspective of non-equipped naive drivers. However, main focus is on non-equipped drivers’ behavioural changes in regard to an imitation of the efficient driving behaviour of an equipped driver. Parameters measuring not only individual behavioural adaptation, but also behavioural adaptation of a platoon of drivers are presented. Thus, a step towards a methodology for studies with multi-driver simulators, especially for the data analysis, is made.

Impact of Intelligent Transportation Systems on Vehicle Fuel Consumption and Emission Modeling: An Overview

Climate change due to greenhouse gas emissions has led to new vehicle emissions standards which in turn have led to a call for vehicle technologies to meet these standards. Modeling of vehicle fuel consumption and emissions emerged as an effective tool to help in developing and assessing such technologies, to help in predicting aggregate vehicle fuel consumption and emissions, and to complement traffic simulation models. The paper identifies the current state of the art on vehicle fuel consumption and emissions modeling and its utilization to test the environmental impact of the Intelligent Transportation Systems (ITS)' measures and to evaluate transportation network improvements. The study presents the relevant models to ITS in the key classifications of models in this research area. It demonstrates that the trends of vehicle fuel consumption and emissions provided by current models generally do satisfactorily replicate field data trends. It shows as well that microscopic modeling is the most accurate type of vehicle fuel consumption and emissions modeling and macroscopic modeling is most helpful in estimating aggregate emissions inventories. In addition, the paper demonstrates that mesoscopic models and empirical models strike a balance between accuracy and simplicity so that they are highly suitable for evaluating the environmental impact of the ITS' measures and transportation network improvements. The study indicates that there is a significant impact of ITS measures on vehicle fuel consumption and emissions rates. It identifies as well the relation among transportation models, cold start excess emission modeling subcategories, and hot-stabilized emission models. The study concludes that no one model as yet fully meets the needs of ITS applications.

The Impact of Intelligent Transport Systems on Office Location Attractiveness: Testing the Predictive Validity of a Location Choice Model

In recent years, a model was developed describing effects of Intelligent Transport Systems (ITS) on location preferences of office-keeping organisations in urbanised. The model is based on a Hierarchical Information Integration Approach, using Stated Preference data. The model indicates that ITS significantly contribute to office location preferences. This article presents an analysis of the predictive validity of that model. Office keeping organisations in two mediumsized Dutch cities (Nijmegen and Arnhem) were asked to evaluate existing office locations assuming different ITS scenarios. The analyses show that the model systematically underestimates the attractiveness of office locations. However, the part worth utility scores, describing the contribution of ITS to location preferences, appeared to be good estimators of the added value of ITS. Moreover, the ranking position that responding organisations give to the office locations in their cities is estimated properly by the model.

Techno-economic assessment of the potential of intelligent transport systems to reduce CO 2 emissions

This study assesses the technical feasibility, economic affordability and social acceptability of low-greenhouse gas (GHG) emissions intelligent transport systems (ITS) that could be implemented in the European road infrastructure over the next 40 years for passenger and freight traffic. Three ITS applications are considered: driver assistance systems and the automated highway system (AHS) for passenger traffic and commercial vehicle operations for freight traffic. These technologies are anticipated to lead to a more efficient use of the existing road network, while potentially reducing GHG emissions generated by vehicles. The impact of ITS on road capacity and vehicle's CO2 emissions is determined. Assessment is based on a reference system, which consists of the average new vehicle in the road network of the EU-27 countries. The various opportunities offered by the selected ITS technologies are specified and the major constraints for reducing GHG emissions are identified. Cost-effectiveness and sensitivity analysis is carried out. The results are validated by a suitably designed expert-based survey. It is found that ITS have the potential to improve capacity and reduce emissions. The most significant benefits are offered by the deployment of AHS. Social acceptability issues such as privacy and liability need to be further addressed to facilitate wide deployment of ITS.

Intelligent transportation systems: an impact analysis for Michigan

AbstractAre Intelligent Transportation Systems (ITS) affecting transportation industry only? ITS are currently perceived as a contribution to transportation industry only; however, by quantitatively analyzing the economic impact of ITS on the state of Michigan, this work attempts to answer the posed question. The quantitative economic analysis is carried out through the well‐established Leontief's Input–Output (I‐O) model. This model is employed to establish ITS effects on each industry by detailing RIMS II I‐O tables for Michigan constructed from the national I‐O tables. Major savings by ITS identified as reduced time delays and fuel cost savings are quantitatively simulated thereby generating an overall cost reduction factor which is incorporated in Michigan I‐O tables to modify their characteristics. ITS impact on each industry in I‐O tables is achieved by maximizing effects on certain selected industries. Impact multipliers that are customary macro‐economic measures for I‐O analysis are then calculated for all the aggregated industries. Multipliers comparison for the three cases namely before ITS implementation, conventional improvement methods, and after ITS implementation is evaluated. These values suggest greater economic benefits that may be achieved by statewide implementation of the ITS. Copyright © 2010 John Wiley & Sons, Ltd.

Economic Impact/Forecast Model of Intelligent Transportation Systems in Michigan: An Input Output Analysis

This study aims to understand the impact that Intelligent Transportation Systems (ITS) may have on the state of Michigan's economy. Econometric aspects of ITS over other industries are sought because presently ITS is perceived as a contribution to transportation industry only. Leontief's Input-Output (I-O) model that establishes an economic measurement through industries is used to ascertain the ITS effects on Michigan. An aggregated industrial I-O model using RIMS II is utilized to compute the Michigan I-O table from the national I-O table. Savings in reduced time delays and fuel costs by employing full ITS products are forecasted to calculate an overall cost reduction factor pertaining to transportation industry. Impact of ITS on the intertwined industries in the I-O model is achieved by modifying the characteristics of Michigan I-O table and maximizing the effect on certain selected industries. Macroeconomic measures are then computed in terms of multipliers for all aggregated industries. These multipliers are obtained for the cases before ITS implementation, conventional improvement methods, and after ITS implementation. A sensitivity analysis is also carried out to gauge the forecast of ITS implementation in the state. Results suggest greater economic benefits that may be achieved for most industries by statewide implementation of the full ITS products.