Towards Improved Traffic Impact Assessments for Construction Sites

To forecast traffic impact upon completing a new proposed development, a report of Traffic Impact Assessment (TIA) is compulsory to be prepared by developers, engineers, and traffic planners. Even though TIA has been practiced ever since, TIA does not seem sustainable as it focuses on car trips instead of other transportation modes. Different than TIA, Transportation Assessment (TA) is another traffic study regarding potential traffic impacts of proposed developments that covers the overall modes of transportation impacts. TA also mitigates any adverse consequences in the aspect of sustainability while highlighting on the car-trips. A comparison is made to finalizes which of the two assessments bring more benefit and preferred in the aspect of sustainability. TA is identified to be more sustainable based on its detail procedures on all modes impacts and could replace TIA to help solve traffic congestions and help achieve the government’s target of 40% modal shift on public transportation.

Development pressure and the need for urban integration in South African towns, caused by spatial segregation due to the impact of policies of the previous political dispensation, gives rise to various ad hoc land development applications in the form of rezoning and township establishment.Land use and transportation planning is integrated and the impact of traffic due to such changes is key in the approval of land development applications.The transition to a democratic society also led to a lack of capacity and practitioners with skills to conduct traffic impact assessments within municipalities.This paper deals with the application of a simplistic procedure to undertake traffic impact assessments (TIAs) in small and intermediate towns where there are restricted levels of data and information available.The research is based on a case study undertaken within the area of influence of the North West University (Potchefstroom Campus) and includes the assessment of traffic impact based on two Town Planning Amendment Scheme applications in the study area.The paper covers existing planning knowledge; analysis of the inter-traffic movements; intra-traffic movements and quantification of the traffic impact based on the proposed land use amendment applications.The paper is concluded by the quantification of the traffic impacts supported by recommendations in order to mitigate the traffic impact within the study area where the demand for spatial transformation is high.The assessment will be based on land use and transport integration principles and impact quantification.

Indian cities are witnessing rampant growth in terms of population and economic growth. The urban population percentage increased from 28.6% in 2001 to 37.7% in 2011. Being a leading IT exporter, Bengaluru sees an increasing number of commercial buildings burdening the existing road infrastructure, leading to heavy congestion in particular areas of Bengaluru. This calls for an understanding of any upcoming commercial development before it is thoroughly established to take adequate measures to reduce the negative impacts. However, the Traffic Impact Assessment (TIA) using the traditional four-stage modeling cannot be used in the current context due to the impact of COVID-19 on the existing traffic. This paper adopts an improved methodological framework that can be used to evaluate the impact of traffic in any situation. An average of 6.6% percentage increase (maximum 25% on some roads) in V/Cs is observed before and after the new development on the adjoining roads. To improve the level of service, several scenarios are considered and analyzed to provide the best mitigation strategies. This improved methodological framework can help to evaluate a new development’s impact for any given situation, and what mitigation strategies and types of transportation improvements may be essential to maintain a smooth moving traffic with a satisfactory level of service.KeywordsTraffic Impact Analysis (TIA)Traffic forecastCommercial land useTravel demand modelScenario analysis

<p>Most of worldwide population lives in urban areas, demanding for air quality information with a high spatio-temporal resolution. The most promising approaches for estimating urban air quality within the complex urban topography are small sensor networks and simulation models.</p><p>The TRAFAIR project focuses on understanding the role of traffic emissions on urban air quality by the combination of dispersion modelling, space- and time-resolved gas monitoring by lower cost sensors and realistic traffic flow rates by dynamic traffic model based on real time traffic data. Test cities of TRAFAIR are Modena, Florence, Pisa, Livorno, Zaragoza and Santiago de Compostela.</p><p>Depending on the size of the urban area, from 6 to 13 sensors units are deployed across each city since August 2019, providing estimates of NO, NO<sub>2</sub>, CO and O<sub>3</sub>, along with RH and temperature. Metal oxide sensors are deployed in Tuscany (Florence, Pisa, Livorno) and electrochemical cells are used elsewhere. The units are calibrated on a regular basis by co-location at the air quality regulatory stations and subsequently deployed across the town to monitor several representative locations (e.g. Low Emission Zones, hospital surroundings). For each sensor the raw readings (e.g. mV for electrochemical cells) are collected and a regression model (e.g. Random Forest) is applied to derive a calibration function, exploiting the data from the regulatory stations during co-location periods; for instance in Modena, the first short-term calibration provided a model with a Mean Absolute Error between 5 – 6 ppb and 2 – 4 ppb for NO and NO<sub>2</sub> respectively.</p><p>The sensors are used for both real-time urban air quality mapping and to test and validate the 24hr forecast service of NOx by the microscale lagrangian dispersion model GRAL. The simulation domains, covering the urban area of each TRAFAIR city, have a horizontal resolution of 4 m and allow to account for the presence of buildings. The dispersion model mainly focuses on NOx by traffic emissions, although domestic heating will be also included in the analysis. Vehicular emissions are based either upon historical traffic data (e.g. induction loops), or upon previously available traffic flow simulation, or upon traffic pattern reconstruction using a traffic flow model followed by a cluster analysis to group streets with similar pattern.</p><p>The final goal of the project is the development of a tool to support local policymakers and to inform citizenship about the quality of air and the impact of urban emission sources, particularly traffic. A secondary goal of the project is the development of a valuable QA/QC protocol for small sensor units and the optimization of the modelling chain for the forecast of traffic and domestic heating impact on local air quality at the urban scale.</p>

Noise pollution arises as a severe problem to the community and ground transportation has highly impacted noise environment of the neighbourhood. In the present study, six stations were selected from three different locations in order to assess the traffic noise impact among residential area along Jalan Kluang, Batu Pahat. The objectives of this study focused on two main stages: (1) determination of noise indices; and (2) assessment of traffic noise impact. The noise indices such as equivalent continuous noise level (LAeq), noise level exceeded 10% (L10), 90% (L90) and the maximum noise level (LAFmax) of peak hours measurement time were determined using sound level meter (SLM) at all six stations. Besides, traffic noise index (TNI) and noise pollution level (LNP) were calculated based on the measured noise indices to expose the traffic noise impact. Overall, the result of the present study showed that residential area faces traffic noise disturbance partially whereby 50% of the stations exceeded the DOE standard which was 55dBA. Nevertheless, the residents living nearby Jalan Kluang are commonly facing traffic noise nuisance, but stations located further inside residential areas tend to have more residential background noises apart from traffic influence.

This paper presents an adaptive traffic signal controller algorithm that allows real time traffic flow data to control traffic signal timing for an isolated intersection. The proposed design allows greater flexibility and control of signal timings while keeping in view multiple criterions. It is based on simple rules that allow traffic flows on all intersection branches to control the timing of current signal phase without causing unrealistic delays on low volume side streets. The algorithm is also adjusted for capacity of different links. Detector data has been used to assess congestion situation and respond accordingly. A priority scheme for priority vehicles is also proposed. As a proof of concept, the proposed algorithm has been implemented on FPGA as a parameterized VERILOG model allowing adjustment to any intersection size and topology. An evaluator circuit was designed to assess the performance as compared to actuated signal controller. Improvement was observed in total stopped delay, number of stops and queue lengths.

This paper discusses the Integrated Database System for Traffic Impact Assessment. Traffic impact assessment requires various data for analysis. However, data are widely scattered and a large amount of time is needed to execute analysis. In order to solve these problems, we developed a system which consists of Web GIS Server and Web DB server. These servers use the Java Server Side Technology. As a result, information retrieval by accessing the system became possible, and the traffic impact assessment can be executed more efficiently. A case study to apply this system to large-scale suburban shopping malls proved the effectiveness of the system.

Framework for Public Transport Integration at Railway Stations and Its Implications for Quality of Life

In pursuing fear-reduction strategies in public transport, the total experience of accessing rail stations should be taken into account. This article correlates passengers’ fear of crime at train stations with neighborhood types and income data within the pedestrian catchment area. The research is based on urban form and income around 84 S-train stations in the Copenhagen metropolitan area and nine years of passenger surveys on fear of crime at these stations. The study reveals a significant positive correlation between low income and fear of crime; the lower the income in an urban area, the more unsafe passengers feel at the station. However, when controlling for the relationship between income, safety and neighborhood type, stations in neighborhoods with urban form associated with low incomes have the lowest ratings of safety. The research indicates that train passengers’ sense of security is connected to neighborhood type and the city’s planning characteristics. This is an important finding for urban designers and planners working on the integration of public transport and station design in urban areas.

Control methodologies of traffic signals have significantly improved during the recent past along with advancements in technology. Adaptive traffic signal control is the most recent and advanced control type of traffic signals. Adaptive control is able to efficiently relieve traffic congestion by continuously adjusting signal timings according to real-time traffic conditions. Conventional optimization methods such as integer programming, hill climbing, or descent gradient searching have been gradually overshadowed by genetic algorithms in many areas including traffic signal operation. The research presented in this article is distinct from previous studies in that it focuses on real-time adaptive signal optimization using genetic algorithms. The proposed adaptive signal system provides acyclic signal operation based on a rolling horizon real-time control approach. The algorithm was tested using microsimulation for on-line evaluation and comparison to fixed-time plans generated from the latest TRANSYT-7F version 9.7, which has a genetic optimization feature. The developed signal system consists of three major components including a genetic algorithm optimization module, an internal traffic simulation module, and a database management system all working in cooperation to optimize signal timings in real time. Using the pseudo on-line simulation platform, three testing scenarios for high, medium, and low level of traffic demands were conducted focusing on evaluating several important features of the proposed adaptive signal control system. The test results indicated that real-time genetic control outperformed fixed-signal timing plan in all scenarios based on total vehicle delay.

Optimizing schedule for improving the traffic impact of work zone on roads

Traffic & environmental impact assessment under distinct operational speeds for automated shuttle bus services

Connect & Drive is a start-up project to develop a cooperative driving system and improve the traffic performance on Dutch highways. It consists of two interactive subsystems: cooperative adaptive cruise control (CACC) and connected cruise control (CCC). To assess the traffic performance, a traffic simulation model will be established for large-scale evaluation and providing feedbacks to system designs. This paper studies the factors determining the traffic performance and discusses challenges and difficulties to establish such a traffic simulation model.

This paper proposes an efficient random access channel (RACH) transmission method that utilizes soft-combining of consecutively retransmitted message data packets according to the quality of service (QoS) requirements for broadband multi-carrier/DS-CDMA (MC/DS-CDMA) in the reverse link. In the proposed scheme, the relative transmission power of message data compared to that of a successfully detected preamble for non-real time (NRT) traffic data is significantly reduced to achieve error-free transmission by soft-combining several retransmitted message data packets thanks to time diversity since the delay requirement is relaxed. Meanwhile, for real time (RT) traffic data with rigid delay requirements, the relative transmission power of the message data compared to that of the detected preamble is increased to reduce the residual packet error rate with a limited number of retransmissions. Simulation results elucidate that the total required average received signal energy per bit-to-background noise power spectrum density ratio (E/sub b//N/sub 0/) for error-free transmission with time diversity for NRT traffic data is reduced by approximately 2.4 dB compared to that for RT traffic data in a 12-path fading channel with exponential distribution at the fading maximum Doppler frequency of f/sub D/ = 20 Hz.

Traffic incidents continue to cause a significant loss in deaths, injuries, and property damages. Reported traffic accident data contains a considerable amount of human errors, hindering the studies on traffic accidents. Several approaches have been developed to detect accidents using traffic data in real time. However, those approaches do not consider the spatiotemporal patterns inherent in traffic data, resulting in high false alarm rates. In this paper, we study the problem of traffic accident detection by considering multiple traffic speed time series collected from road network sensors. To capture the spatiotemporal impact of traffic accidents to upstream locations, we adopt Impact Interval Grouping (IIG), which compares real-time traffic speed with historical data, and generates impact intervals to determine the presence of accidents. Furthermore, we take a multivariate time series classification approach and extract three novel features to measure the severity of traffic accidents. We use real-world traffic speed and accident datasets in our empirical evaluation, and our solutions outperform state-of-the-art approaches in multivariate time series classification.