Intelligent Transportation Systems Strategies Research Articles

Emissions and Fuel Consumption Modeling for Evaluating Environmental Effectiveness of ITS Strategies

Road transportation is a major fuel consumer and greenhouse gas emitter. Recently, the intelligent transportation systems (ITSs) technologies, which can improve traffic flow and safety, have been developed to reduce the fuel consumption and vehicle emissions. Emission and fuel consumption estimation models play a key role in the evaluation of ITS technologies. Based on the influence analysis of driving parameters on vehicle emissions, this paper establishes a set of mesoscopic vehicle emission and fuel consumption models using the real-world vehicle operation and emission data. The results demonstrate that these models are more appropriate to evaluate the environmental effectiveness of ITS strategies with enough estimation accuracy.

Study on Eco-Route Planning Algorithm and Environmental Impact Assessment

The transportation sector has faced increased environmental pressure due to the rapid motorization in China over recent years. In addition to energy policies and the use of fuel-efficient vehicles, CO2 emissions and fuel consumption can be lowered through the application of intelligent transportation systems (ITS) technologies that can affect drivers’ route choice decision-making process and reduce congestion. Ecological route (eco-route) planning is one such strategy that provides the least CO2 emission and fuel consumption routes for vehicles. In this article, a set of mesoscopic vehicle emission and fuel consumption models are established based on locally collected vehicle operation and emission data, and these models are more appropriate for evaluating the effectiveness of ITS strategies with sufficient estimation accuracy. Based on the proposed models and a dynamic traffic information database, an eco-route planning algorithm is proposed as well, which is expected to be consistent with the road network characteristics of China cities. Furthermore, the environmental impact of the eco-route is fully evaluated with tests undertaken in Beijing urban areas. The results show that the proposed eco-route algorithm significantly reduces fuel consumption and has good environmental performance.

Measuring Sustainability: How Traffic Incident Management through Intelligent Transportation Systems has Greater Energy and Environmental Benefits than Common Construction-Phase Strategies for “Green” Roadways

ABSTRACT The research presented in this paper compares the direct emissions and fuel consumption savings of five different strategies for greener roads. Specifically, savings from an incident management strategy using an Intelligent Transportation Systems (ITS) application, traffic cameras, is compared to savings from strategies focused on the construction phase including; using regionally provided materials, reducing fossil fuel use, reusing pavement, and using warm-mix asphalt. The comparison used lifecycle assessment to evaluate a 10-mile segment of a key urban interstate in South Carolina. The results revealed that within the first year of operations, implementing the selected ITS strategy would provide a fuel savings that is over 3 times greater than the combined savings that could be realized by implementing all 4 construction-phase strategies together. For an 8-year repaving schedule, the ITS strategy provides fuel savings over 30 times larger than all the construction phase strategies combined, and reduces carbon-dioxide emissions over 5 times more than any one of the construction-phase strategies. The article highlights ITS's effectiveness as a sustainability tool by comparing emissions and fuel use reductions to sustainable construction strategies. These results suggest that policies and rating systems for more sustainable roads should weigh ITS strategies considerably more than individual construction-phase strategies. The results also highlight the need for transportation policy to evaluate the benefits of all potential sustainable strategies to encourage implementation of the most effective ones.

Estimating Operational Impacts of Freeway Work Zones on Extended Facilities

This paper presents an approach to estimating the operational impacts of freeway work zones. The focus is on significant work zones on freeway corridors as defined by FHWA. The methodology is based on deterministic freeway capacity concepts described in the Highway Capacity Manual and allows the analyst to test impacts of a range of work zone configurations in an extended time–space domain. The focus on extended facilities refers to the analysis of multiple segments of various types, including basic freeway, ramp, and weaving segments. The cost-effective and time-efficient analysis approach can model effects of work zones such as lane closures, lower speed limits, capacity reduction, and the implicit effects of traffic diversion, peak reduction, and other intelligent transportation system strategies. Performance measures include total delay, queuing impacts, average running speed, and the potential to estimate user cost. The methodology focuses on a corridor-based analysis of work zones on freeways. It is not appropriate for network analysis, for long corridors, for signalized arterials, or for cases in which congestion on the arterial network significantly affects freeway operations. In these applications, a simulation-based analysis is more appropriate. However, the methodology presented in this paper is more cost-effective and less data intensive than simulation analysis and can readily be calibrated with local or regional data. This is a key advantage in challenging economic times, when agencies struggle to balance a constrained budget with the need to analyze multiple construction projects within their jurisdiction.

Economic evaluation of intelligent transportation systems strategies: the case of the Oslo toll cordon

Intelligent transportation systems (ITS) projects often offer an effective solution for many transportation problems as compared to traditional solutions such as increasing road capacity to solve congestion problems. However, ITS projects are often not evaluated in economic terms either because methodologies are not available, or when they are, data become a limitation. Since economic assessment is the order of the day in the transportation sector, many potential ITS projects are losing terrain relative to alternative solutions. This study develops and demonstrates that economic evaluation of ITS projects in the road sector is possible. The authors use the Oslo toll cordon as a case example, where semi-automatic toll collection was replaced by a full-fledged ITS-based toll collection. The results show that ITS-based toll collection is highly profitable from an economic point of view. The reasons are that the ITS-based toll collection relieves congestion at toll points thereby leading to (i) time savings for road users, (ii) reduced noise and pollution associated with slow movement of vehicles at toll points, (iii) improvement of the city's landscape because toll booths and signposts are removed and (iv) reduced costs associated with operating tolls. The study urges ITS proponents to start using economic evaluation when assessing the merits of their projects.

Application of a simulation-based dynamic traffic assignment model

The evaluation of on-line intelligent transportation system (ITS) measures, such as adaptive route-guidance and traffic management systems, depends heavily on the use of faster than real time traffic simulation models. Off-line applications, such as the testing of ITS strategies and planning studies, are also best served by fast-running traffic models due to the repetitive or iterative nature of such investigations. This paper describes a simulation-based, iterative dynamic equilibrium traffic assignment model. The determination of time-dependent path flows is modeled as a master problem that is solved using the method of successive averages (MSA). The determination of path travel times for a given set of path flows is the network-loading sub-problem, which is solved using the space-time queuing approach of Mahut. This loading method has been shown to provide reasonably accurate results with very little computational effort. The model was applied to the Stockholm road network, which consists of 2100 links, 1191 nodes, 228 zones, representing and 4964 turns. The results show that this model is applicable to medium-size networks with a very reasonable computation time.

REDUCING GREENHOUSE EMISSIONS AND FUEL CONSUMPTION: Sustainable Approaches for Surface Transportation

Climate change is rapidly becoming known as a tangible issue that must be addressed to avoid major environmental consequences in the future. Recent change in public opinion has been caused by the physical signs of climate change–melting glaciers, rising sea levels, more severe storm and drought events, and hotter average global temperatures annually. Transportation is a major contributor of carbon dioxide (CO2) and other greenhouse gas emissions from human activity, accounting for approximately 14 percent of total anthropogenic emissions globally and about 27 percent in the U.S. Fortunately, transportation technologies and strategies are emerging that can help to meet the climate challenge. These include automotive and fuel technologies, intelligent transportation systems (ITS), and mobility management strategies that can reduce the demand for private vehicles. While the climate change benefits of innovative engine and vehicle technologies are relatively well understood, there are fewer studies available on the energy and emission impacts of ITS and mobility management strategies. In the future, ITS and mobility management will likely play a greater role in reducing fuel consumption. Studies are often based on simulation models, scenario analysis, and limited deployment experience. Thus, more research is needed to quantify potential impacts. Of the nine ITS technologies examined, traffic signal control, electronic toll collection, bus rapid transit, and traveler information have been deployed more widely and demonstrated positive impacts (but often on a limited basis). Mobility management approaches that have established the greatest CO2 reduction potential, to date, include road pricing policies (congestion and cordon) and carsharing (short-term auto access). Other approaches have also indicated CO2 reduction potential including: low-speed modes, integrated regional smart cards, park-and-ride facilities, parking cash out, smart growth, telecommuting, and carpooling.

Using Microscopic Simulation to Evaluate Potential Intelligent Transportation System Strategies Under Nonrecurrent Congestion

A microsimulation method is presented for evaluating the effectiveness of potential intelligent transportation system (ITS) strategies under nonrecurrent congestion. The evaluated ITS strategies include incident management, adaptive ramp metering, traveler information systems, arterial management, and a combination of those strategies. These strategies are implemented and evaluated over a road network in Irvine, California, with the microsimulation model PARAMICS. The evaluation results show that all ITS strategies have positive effects on network performance. Because of the network topology (one major freeway with two parallel arterial streets), real-time traveler information has the greatest benefits among all single ITS strategies. However, a combination of ITS strategies can further increase benefits.

Application of Accident Prediction Models for Computation of Accident Risk on Transportation Networks

Accident risk has been applied extensively in transportation safety analysis. Risk is often used to describe the level of safety in transportation systems by incorporating a measure of exposure, such as traffic flow or kilometers driven. The most commonly applied definition of accident risk states that risk is a linear function of accidents and traffic flow. This definition, however, creates problems for transportation systems that are characterized by a nonlinear relationship between these variables. The primary objective of the original research was to illustrate the application of accident prediction models (APMs) to estimate accident risk on transportation networks. (APMs are useful tools for establishing the proper relationship between accidents and traffic flow.) The secondary objective was to describe important issues and limitations surrounding the application of APMs for this purpose. To accomplish these objectives, APMs were applied to a computerized transportation network with the help of EMME/2. The accident risk was computed with the traffic flow output of the computer program. The results were dramatic and unexpected: in essence, the individual risk of being involved in a collision decreases as traffic flow increases. The current and most common model form of APMs explains this outcome. The application of these results may have significant effects on transportation policy and intelligent transportation system strategies.

Temporal and Spatial Variations of Real-Time Traffic Data in Urban Areas

Variations in traffic flow patterns can be very useful in helping intelligent transportation systems (ITS) provide drivers with useful and accurate route and travel-time information, in examining the potential benefits of flexible work hours, and in assessing the environmental effects of traffic congestion. Much of the work done toward examining temporal and spatial variations in traffic flow has concentrated on freeways, largely ignoring urban areas, where ITS strategies can have the most important effects. Addressed are the issues of temporal and spatial distributions and variations in real-time traffic flow by using data from 140 loop detectors from the greater Athens, Greece, urban area. The results indicate that the temporal distributions of traffic flows are nonnormal, and they vary by direction (toward, in, or away from the central business district) and time of day. This result has important implications in correctly calibrating the real-time simulation models used for volume and travel-time predictions. The results also indicate that traffic in Athens exhibits low seasonality, with lower traffic during the summer months, although traffic conditions are very similar for all weekdays but vary relative to the weekends and by time of day.

Measuring Aggregate Productivity Benefits from Intelligent Transportation System Applications: The California Experience

A stated purpose of intelligent transportation system (ITS) applications has been to increase the productivity of the existing transportation system. Such productivity increases, should they occur, would be an important input into any benefit-cost analysis for the evaluation of ITS applications. The impact of different ITS applications on highway productivity in California is investigated. Using time series data for California counties, two forms of analysis are undertaken. The first examines ITS applications using a production function model, and the second uses total factor productivity (TFP) techniques to find the regional aggregate benefit of ITS strategies. The results of these two analyses are particularly interesting, in part because ITS impacts have not been evaluated in this way before. The production function model indicates that there is a slight advantage to the economy in a particular region due to ITS services. Findings also indicate that this advantage is a result of aggregate growth and not merely redistributive effects. The TFP analysis indicates that ITS applications contribute to regional economies and county product as well, though these results are only significant at the 10 percent level. Whereas the findings indicate an apparent need to include economic benefits in any study of ITS projects, further study in this area is needed to draw more concrete conclusions. In particular, the notion of synergies between multiple projects of the same type or different types is one of the next steps to be taken.

Requirements for Evaluating Traffic Signal Control Impacts on Energy and Emissions Based on Instantaneous Speed and Acceleration Measurements

The evaluation of many transportation network improvements commonly is conducted by first estimating average speeds from a transportation or traffic model and then converting these average speeds into emission estimates based on an environmental model such as MOBILE. Unfortunately, recent research has shown that certainly average speed and perhaps even simple estimates of the amount of delay and the number of stops on a link are insufficient measures to fully capture the impact of intelligent transportation system strategies such as traffic signal coordination. In an attempt to address this limitation, the application of a series of multivariate fuel consumption and emission prediction models is illustrated, both within a traffic simulation model of a signalized arterial and directly to instantaneous speed and acceleration data from floating cars traveling down a similar signalized arterial. The application of these multivariate relationships is illustrated for eight light-duty vehicles, ranging in size from subcompacts to minivans and sport-utilities using data obtained from the Oak Ridge National Laboratory. The objective is to illustrate that the application of these instantaneous models is both feasible and practical and that it produces results that are reasonable in terms of both their absolute magnitude and their relative trends. This research is one step in a more comprehensive modeling framework for dealing with the impacts of intelligent transportation systems on energy consumption and vehicle emissions. Other steps include analyses of traffic diversion and induced demand and validation of the estimated fuel consumption and emissions using direct on-road measurements.

Individual Vehicle Speed Estimation Using Single Loop Inductive Waveforms

Travel time is the reciprocal of speed and is a useful measure of road congestion and traffic system performance. Travel time is also a basic traffic variable that is used in many intelligent transportation system strategies such as route guidance, incident detection, and traveler information systems. Previously, speeds were mainly acquired from double inductive loops configured as speed traps, because single loop speed estimates based on assumptions of a constant vehicle length were inaccurate. However, more accurate measurements of speed can now be accomplished with single loops by utilizing inductive waveforms of vehicles that are output from newer detector cards. An algorithm using signal processing and statistical methods was developed to extract speeds from inductive waveforms. The results show that the proposed algorithm performs better than conventional single loop estimation methods. The results also show that the algorithm is robust under different traffic conditions and is transferrable across surveillance sites without the need for recalibration. The use of the extensive single loop surveillance infrastructure is a cost-effective way of obtaining more accurate networkwide travel time information.