Impact Analysis of Energy and Emissions in Lane-Closure-Free Road Inspections

Modelling instantaneous traffic emission and the influence of traffic speed limits

The presence of work zones due to pavement repair and rehabilitation is very common in highway facilities. Lane closures associated with work zones result in capacity reduction, which, in turn, often leads to increased congestion at such locations. This paper documents findings from a study that investigated the performance of freeway facilities in the presence of work zones under various Temporary Traffic Control (TTC) and lane closure scenarios while taking under consideration traffic composition and driving behaviors. The study site was an approximately 10-mile freeway segment of Interstate 65 (I-65) located in Birmingham, AL. The testbed was coded in PTV VISSIM, a microscopic simulation analysis platform, for: 1) baseline conditions (i.e., no work zone presence) and 2) work zone conditions with single lane closure (i.e., 3-to-2 lane closure). Work zone scenarios were coded for two TTC strategies, namely, early merge and late merge control and for three different positions of the lane closure (i.e., left, right, and center lane closures). The length of the work zones varied from 1000 to 2000, and 3000 ft. Sensitivity analysis was performed to document the operational impacts of varying heavy vehicle percentages, changes in drivers’ aggressiveness, and projected traffic demand changes. The impacts were quantified using linked-based measures of effectiveness (MOEs) such as travel time, and travel time index. The study results show that there is no significant change in travel time index due to the variation of work zone length across the study corridor. Under similar traffic control and demand conditions, a center lane closure consistently results in significantly higher travel time index than a left or right lane closure and should be avoided. Consideration of operational impacts of changes in truck percentage indicates that the corridor can absorb an increase in truck percentage from 10% to 15%, while performance rapidly deteriorates when a higher percentage of trucks is present in the traffic stream. The study findings can be used to guide transportation agencies in their future efforts to develop strategic lane closure plans that minimize congestion.

With the continuous process of urbanization in many cities around the globe, construction works are increasingly interrupting traffic flow in urban roads. At construction sites, usually traffic control personnel are employed to instruct the traffic flow at the times obstruction. Employing manual labour flagmen for traffic control is costly and can potentially expose the workers to safety hazards. Alternative attempts such as using remotely controlled signs or timers are not ideal; the former still requires human input and the latter does not optimize traffic control based on traffic density. To tackle this problem, we propose a fully automated traffic controller solution that uses visual sensors and machine vision technology. This system can replace the human traffic controllers during lane closures on the event of a construction. The implementation of this system eliminates the requirement for conventional traffic controllers, reducing the overall health and safety risks to workers and drivers. Our solution can result in substantial cost savings by replacing the traffic controllers. In addition, with manual labour, traffic control performance can be affected by weather conditions or fatigue from long hour shifts. Thus, our solution can lead to improved road safety and robustness of traffic flow as well.

Traffic congestion on highways causes decreased efficiency in road operation and increased energy consumption and environmental pollution. Various measures for traffic control have been considered for easing traffic congestion. In this paper, we propose the use of ramp metering control by introducing the reinforcement learning model in artificial intelligence, which is then combined with a dynamic micro traffic style simulation. Numerical simulation showed that when effective Reinforcement Learning ramp metering control is completed in the on-ramp section, traffic confusion can be prevented in advance.

Structural Safety Assessment and Traffic Control Strategies of Widened Highway Bridges under Maintenance Works Requiring Partial Lane Closure

As a result of lane closures during rail line construction, traffic conditions inevitably deteriorate and cause traffic congestion. This paper proposes a paradigm for identifying optimal lane closures to maximize the average speed (V_avg) in the construction area. The framework begins with creating a simulation model to examine the traffic impact of each lane closure alternative and applies the Box-Behnken design technique to limit the number of possible scenarios. The case study for this research was the six-lane (three lanes in each direction) Road 304 in Bangkok, Thailand. The results confirmed that the partial lane closure alternatives would increase V_avg by at least 5% from having one lane fully closed. The optimal construction spacing in the case study was 1.5 km to obtain the maximum V_avg. However, the optimal lane closure length occurred when its value was close to 0 km, which is impractical. Thus, the paper recommends a lower threshold for the lane closure length of 1.4 km compared to other literature. Furthermore, the longest possible lane closure should be designated for light traffic volumes, whereas a dynamic speed limit should be implemented to alternate the speed limit during peak and off-peak periods for high traffic volumes.

Road Network Traffic Congestion Evaluation Simulation Model based on Complex Network

To monitor air pollution on roads in urban areas, it is necessary to accurately estimate emissions from vehicles. For this purpose, vehicle emission estimation models have been developed. Vehicle emission estimation models are categorized into macroscopic models and microscopic models. While the calculation is simple, macroscopic models utilize the average speed of vehicles without accounting for the acceleration and deceleration of individual vehicles. Therefore, limitations exist in estimating accurate emissions when there are frequent changes in driving behavior. Microscopic emission estimation models overcome these limitations by utilizing the trajectory data of each vehicle. In this method, the total emissions in a road segment are calculated by adding together the emissions from individual vehicles. However, most research studies consider the total vehicle emissions in a road section without considering the difference in vehicle emissions at different locations of a selected road section. In this study, a road segment between two intersections was divided into sub-sections, and energy consumption and emission generation were analyzed. Since there are unique driving behaviors depending on the section of the road segment, energy consumption and emission generation patterns were identified. The findings of this study are expected to provide more detailed and quantitative data for better modeling of energy consumption and emissions in urban areas.

Irrigation water and energy saving in well irrigation district from a water-energy nexus perspective

The present study investigates the energy, environment and growth nexus for a panel of South Asian countries including Bangladesh, India, Pakistan, Sri Lanka and Nepal. The simultaneous analysis of real GDP, energy consumption and CO2 emissions is conducted for the period 1980–2010. Levin panel unit root test and Im test panel unit root both indicate that all the variables are I (1). In addition, Kao’s panel Cointegration test specifies a stable long-term relationship between all these variables. Empirical findings show that a 1 % increase in energy consumption increases output by 0.81 % in long run whereas for the same increase in CO2 emission output falls by 0.17 % in long run. Panel Granger causality tests report short-run causality running from energy consumption to CO2 emissions and from CO2 emissions to GDP.

The presence of highway construction zones hinders mobility and affects traffic operations. A 2002 study by Wunderlich & Hardesty reported that nearly 20% of the National Highway System roads have scheduled construction work during the peak construction season. Additionally, approximately 24% of non-recurring delays on freeways are caused by work zones. To minimize time lost by travelers due to work zone induced traffic congestion, it is important to efficiently plan temporary traffic control (TTC) at work zones. Earlier research conducted by Sisiopiku & Ramadan, 2017 confirms that the majority of State Departments of Transportation currently rely on their earlier experience when planning for work zones, rather than consider operational and safety impacts. Using a study corridor in Birmingham, Alabama as a test bed, this study investigated the operational impacts of TTC options for work zones with 3-to-1 lane drop configuration using traffic data collected from the Alabama Department of Transportation. The VISSIM simulation platform was used to conduct the experiments. The experimental design considered two TTC strategies (i.e., static late and early merge) under 3-to-1 lane drop configuration for work-space length of 500 ft for long- and short-term lane closures. The study concluded that the 3-to-1 lane-drop configuration should not be scheduled for long-term duration. Maintenance work can be scheduled from midnight to early morning and under the 3-to-1 lane closure scenario the performance of early and late merge traffic control is similar. Overall, this study used simulation modeling to compare the effectiveness of two traffic control strategies at work zones on the basis of different performance measures. The results provide information about the impact of each control strategy on density, speed, travel time etc. They also help determine what time of the day is best for lane closings in order to reduce adverse impacts from capacity reduction. Thus, the findings are expected to provide valuable guidance for agencies responsible for planning, design, and operations of work zones in the future.

Traffic jams and congestion are a global challenge for smart cities. Digital transformation is touching all aspects of modern-day life utilities, from energy to health services and from education to smart mobility. A smart city aims to cut carbon dioxide emissions while saving lives by reducing driver or passenger trip time, traffic congestion and accidents. Traffic simulations and advanced traffic management systems attempt to address the urbanization infrastructure, sustainability, and mobility issues. Many road infrastructures are decades old and are therefore prone to an excess of frequent and recurrent traffic bottlenecks. This could be attributed to an increase in driver numbers and higher travel demands, which at the same time is increasing in the overall travel duration, air pollution and road accidents. The drivers of the United States alone travel 4.8 trillion kilometers per year. A smart city targets the efficient and sustainable use of its resources and services by using intelligent and interactive management. With the rapid growth of population density, the use of private cars and public transport, scientists thrive on trying to reduce urban traffic congestion in order to efficiently facilitate access to work, education, emergency services, entertainment and other services. For this purpose, graph algorithms and multi-agent system simulations are used for traffic optimization. While urban traffic congestions cannot be modeling eliminated, several frameworks for traffic simulations were developed to reduce traffic congestion. This paper will investigate and provide a comprehensive review of several frameworks used in these traffic agent simulations that can be found in open literature. It will make a comparative analysis of these frameworks used.

Data-driven analysis of battery electric vehicle energy consumption under real-world temperature conditions

Urban residential energy switching in China between 1980 and 2014 prevents 2.2 million premature deaths

<div class="section abstract"><div class="htmlview paragraph">A new generation of vehicle dynamics and powertrain control technologies are being developed to leverage information streams enabled via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connectivity [<span class="xref">1</span>, <span class="xref">2</span>, <span class="xref">3</span>, <span class="xref">4</span>, <span class="xref">5</span>]. While algorithms that use these connected information streams to enable improvements in energy efficiency are being studied in detail, methodologies to quantify and analyze these improvements on a vehicle have not yet been explored fully. A procedure to test and accurately measure energy-consumption benefits of a connected and automated vehicle (CAV) is presented. The first part of the test methodology enables testing in a controlled environment. A traffic simulator is built to model traffic flow in Fort Worth, Texas with sufficient accuracy. The benefits of a traffic simulator are two-fold: (1) generation of repeatable traffic scenarios and (2) evaluation of the robustness of control algorithms by introducing disturbances. The traffic simulator is interfaced with a chassis dynamometer on which the real vehicle will be tested. Algorithms leverage information from the traffic simulator to produce control policies that are optimal in energy consumption. The control policy results in a specific speed of the “ego” vehicle. This speed is relayed back to the traffic simulator, which coordinates movement of the vehicles surrounding the ego vehicle. The second part of the test methodology analyzes energy consumption improvements enabled via CAV technologies. It is expected that the energy consumption reduction realized on a vehicle will differ from simulation studies that rely on simplified models. An advanced instrumentation and measurement scheme enables in-situ efficiency calculations across the powertrain by analyzing energy flows during transient operation of the vehicle. Preliminary results from vehicle testing on a chassis dynamometer are presented.</div></div>