Changes In Traffic Volume Research Articles

Predicting habitat functionality using habitat network models in urban planning

Recently, the European Union adopted a Nature restoration law that aims to stop the ongoing decline of biodiversity and even bring back nature to cities. This is challenging as urbanization is an ongoing process that thrives for more land and densification. In this paper we describe a new Open-Source GIS-tool that automates habitat network analyses and simultaneously generates several maps that can be used for assessments, targeting species survival in urban environments. One result of particular interest is the habitat functionality map that combines values of habitat quality and connectivity. We tested the tool’s ability to predict habitat functionality using amphibian occurrence data observed from the city of Gothenburg in Sweden. Our evaluation shows that habitat functionality was generally a good predictor of amphibian distribution. However, the predictability was sensitive to the cartographic representativity of the input biotope map used. Also, predictions of habitat functionality improved when estimating dispersal probabilities using the Cost-distance algorithm, compared to when using Euclidean distance from reproductive habitats. This finding supports the need to use connectivity models that are responsive to variation and changes in roads, traffic volumes and buildings when performing effect analyses on biodiversity in cities. Finally, we demonstrate how the tool can be used to easily identify areas where restoration measures can effectively increase habitat functionality for target species. This can help planners to find efficient solutions for increasing biodiversity within urban areas.

Dissipative Structure Properties of Traffic Flow in Expressway Weaving Areas

Expressway weaving areas meet dissipative structure characteristics. When traffic states reach a certain range, they exhibit self-organising criticality, and slight changes may trigger unpredictable congestion. This paper examines the correlation between the dissipative structure of the weaving area and key traffic parameters. The range of dissipative structure states in the weaving area is defined through the dissipative structure concept with three-phase traffic flow theory and real traffic data. Based on the fundamental diagram and measured traffic data, the weaving area dissipative structure model characterising the relationship between critical state changes in traffic volume is constructed and validated. Finally, the Cell Transmission Model simulation was used to examine the characteristic relationship between the weaving area dissipative structure state duration, the weaving area length and the weaving flow ratio. The results show that the length of the dissipative structure state is maintained when the traffic flow is self-organised into a free-flow or a congested state positively correlates with the length of the weaving area. Higher weaving flow ratios lead to shorter dissipative structure state durations during congestion formation, and the exact opposite during congestion evacuation. This paper is important for analysing the congestion mechanism and managing congestion.

COVID-19 and speeding: Results of population-based survey of ontario drivers

Introduction: During COVID-19, increased speeding was observed in many jurisdictions. Yet, evidence is limited on what factors predicted increased speeding during the pandemic. This study’s purpose was to examine speeding, and person and situation factors associated with increased speeding since the start of the pandemic. Methods: An online panel survey sampled 1,595 drivers using sex, age, and region quota sampling and weighting to approximate the Ontario, Canada adult population. Measures included: (1) person factors: socio-demographics (age, sex, region); psychological trait of risk propensity (Competitive Attitudes Toward Driving Scale (CATDS)); psychological states (distress - general and COVID-19-related); and behaviors (kilometers driven, alcohol use, police stops and collisions); and (2) COVID-19-related situation factors: perceived changes in (traffic volume, police enforcement). Results: 67.2% of respondents reported speeding; 7.2% reported increased speeding since the start of the pandemic. Bivariate analyses indicated that person factors of younger age, male sex, higher CATDS, higher distress, more alcohol use, more kilometers traveled, police stops, and collisions since the start of the pandemic were associated with increased speeding. Situation factor of perceived less traffic volume since the start of the pandemic was associated with increased speeding. Logistic regression analysis identified odds of reported increased speeding during the pandemic was significantly higher for drivers with higher scores on the CATDS, higher kilometers traveled, and more alcohol use during the pandemic. Conclusions: These findings suggest that higher risk propensity as well as the more kilometers driven and increased alcohol consumption were risk factors for increased speeding. Practical Applications: COVID-19-related factors of lower traffic volume and enforcement are less predictive of increased speeding than driver personality and pandemic-related behaviors of more driving and drinking. Interventions to reduce speeding still need to focus on these person factors through education, enforcement, and strong sanctions for speeding.

Training a Graph Neural Network to solve URLLC and eMBB coexisting in 5G networks

Coexistence of enhanced mobile broadband and ultra-reliable low latency communication in 5G networks is a challenging problem due to the conflicting requirements. In this paper, we decompose the problem into eMBB and URLLC resource allocation phases. For the first phase we propose a heuristic algorithm with O(n) runtime and prove its efficiency and optimality under min–max fairness paradigm. For the URLLC resource allocation, the puncturing framework is adopted and a novel approach using the Graph Neural Networks is proposed to maximize eMBB data rates and fairness while minimizing URLLC outage probability. We show that the runtime of this GNN-based algorithm is also O(n). To train the GNN, an application-specific loss function is designed and empirically shown to be convergent. Our simulation results show that our proposed approach performs very well in terms of eMBB data rates, fairness, and URLLC outage probability in comparison to a number of thoughtfully chosen baselines. We also demonstrate that the proposed GNN is robust to changes in network topology and traffic volume. As we show our algorithm has O(n) runtime, it is fully practical for solving the resource allocation problem in the very short time spans that are required by 5G and future generation networks.

Development, practical challenges, and application of a state-wide transport model system in Australia

ABSTRACT This paper develops a regional strategic travel model system to predict expected changes in traffic volumes and public transport patronage up to 2056. On the supply side, we develop transport network datasets for four linehaul modes – train, coach, car, and plane, incorporating travel times, fares, costs, and service frequency. On the demand side, an aggregate modal share logit model for three trip purposes is estimated to identify the role of various trip attributes and socioeconomic characteristics to forecast modal shares in the base year 2016. The resulting models produce an accessibility index asess each mode role in defining the accessibility to each Statistical Area level 2 in New South Wales and the Australian Capital Territory. This index informs a residential population model to identify the relationship between population and accessibility, highlighting the wider impact of transport improvements on the regional economy. The model links population changes to travel demand and predicts induced demand under a business-as-usual scenario. The application of the model system is illustrated using a corridor between Sydney and Newcastle.

Effect of 20 mph speed limits on traffic injuries in Edinburgh, UK: a natural experiment and modelling study

IntroductionThere is limited research evaluating 20 mph speed limit interventions, and long-term assessments are seldom conducted either globally or within the UK. This study evaluated the impact of the phased...

Traffic transfer between social media and E-commerce platform: the role of social media affordances

Traffic transfer between social media and E-commerce platform: the role of social media affordances

APPLICATION OF THE VISSIM PROGRAM IN EVALUATION AND IMPROVING THE PERFORMANCE OF UNSIGNALED INTERSECTIONS IN PONTIANAK CITY

As part of transportation infrastructure, road infrastructure is essential in increasing community mobility and accessibility. When traffic volume increases or changes its characteristics, the intersection is the first thing that shows the inability to serve the increased traffic. Because intersections are critical points of the traffic system where vehicles from different directions meet, obstacles and traffic jams at intersections will affect the capacity of the road concerned, so the level of service at the intersection will decrease, including the speed, safety, and comfort of road users.This research aims to evaluate the performance of the third and fourth unsignaled intersections in Pontianak City (with a case study of the intersection of Jl. Sultan Abdurrahman-Jl. Sulawesi and the intersection of Jl. Alianyang-Jl. Putri Dara Nante-Jl. Putri Dara Hitam) and analyze alternatives to improve the performance of unsignalized intersections using Manual Kapasitas Jalan Indonesia and the VISSIM program application.The intersection performance analysis results for existing conditions at the third unsignaled intersection have a level of service value of A, and at the fourth unsignaled intersection, a level of service value of B. Based on the intersection performance analysis results, the intersection in its existing condition does not require any treatment or recommendations to improve its performance road infrastructure.

Tempo-spatial analysis of pedestrian movement in the built environment based on crowdsourced big data

Over the years, the urban planning literature has focused substantial attention on walkability research, aiming to enhance physical activity and sustainable communities through urban planning and design. While motorized traffic research has gained momentum in combining innovative technologies for traffic monitoring, the common tools used to monitor pedestrian movement (PM) to this date are limited in time and scale. This study aims at analyzing tempo-spatial dynamics of walking behavior, while utilizing an emerging technology of Bluetooth sensors. We analyzed over 53 million pedestrian records, monitored in 83 street-segments in Tel-Aviv, Israel. The data was recorded for five months, including the time of COVID-19's first lockdown. We showcase tempo-spatial dynamics of PM through different times of the year, while discussing changes in walking traffic volume, walking patterns (frequency peaks), traffic at commercial vs. residential streets, popular street segments and land-uses, and possible applications to urban planning and design. Finally, we discuss the data limitations and challenges for PM monitoring and research. The study shows that BT sensor technology can provide the municipality and decisionmakers with insights on pedestrians' behavior and preferences in real-time and at street-level, enabling to locate infrastructure investments more efficiently and to support planning decisions. The preliminary results of this study suggest further use of this technology for pedestrian movement monitoring and research in urban networks.

Operation standards for exclusive bus lane on expressway using simulation and traffic big data

<abstract> <p>Korea operates exclusive bus lanes (XBLs) on many of its expressways. As XBLs convert one lane of regular-use traffic into bus-only traffic, they have a large impact on traffic flow, so careful judgment is required to determine if the operation is effective. However, XBLs have been operated based on only political judgment due to the lack of standards for the operation of expressway XBLs. Therefore, we sought to establish standards for the operation of expressway XBLs using the micro-traffic simulation program VISSIM and the multi-criteria value function methodology. Various scenarios were established based on traffic volume changes of passenger vehicles and bus traffic of four- and five-lane expressway networks. Through an expert survey, the average speed and speed-deviation values, which are the criteria for evaluating operational efficiency and safety, were determined. Also, value points were converted using average speed and speed deviation extracted from the simulation. In addition, quantitative operation standards were established using the converted value scores. Using the results of this study, we established standards for the operation of the XBLs and presented guidelines for related agencies such as police, bus groups, and corporations. The National Police have prepared guidelines for the operation of the XBLs. Citing the results of this study, the new guidelines were implemented in February 2021, and sections of some XBLs have been abolished. Through this study, quantitative standards for the operation of XBLs, one of the management lane techniques necessary for sustainable highway operation, were prepared and applied to actual highways. By properly applying the newly applied guidelines according to quantitative standards, there will be effects of reducing traffic congestion, improving travel time, and enhancing environmental characteristics such as exhaust gas emission. It is also expected to have a positive effect on safety.</p> </abstract>

АНАЛИЗ АДАПТИВНЫХ СИСТЕМ УПРАВЛЕНИЯ ДОРОЖНЫМ ДВИЖЕНИЕМ

This paper describes state-of-the-art adaptive control systems both in Split Cycle Offset Optimization (SCOOT), Optimization Policies for Adaptive Control (OPAC), Real-Time Hierarchical Optimized Distributed and Effective System (RHODES), Adaptive Control System-Lite (ACS-Lite), and worldwide. Urban road conditions and traffic signal timing to accommodate traffic volume changes and minimize stops and delays are controlled by adaptive traffic control systems on roadways. Based on the research objectives and comparing the positive aspects of past experiences will help traffic engineers and decision makers to select the most effective adaptive control system.

High exposure of ultrafine particles at Guangzhou bus stops and the impact of urban layout

Urban bus stops have been identified as hotspots for particulate matter due to intense vehicle activities, including acceleration from standstill, this poses a risk of short-term exposure to high levels of pollution for residents who wait for buses during their daily commute. In order to provide constructive and scientific recommendations for the placement of bus stops from the perspective of regional architectural layout, we conducted a comparative observational experiment in Guangzhou to investigate the exposure to ultrafine particle (UFP) at bus stations with different building layouts. Our study found that there was a significant interaction between the urban layout and vehicle activities of bus stations with 99% confidence level. The average level of UFP pollution at the street canyon bus stations was 48.2 103ptccm−1 in the afternoon. Pedestrians waiting at the bus stations built in the street canyon could be exposed to excessive UFP levels (higher than 30.0103ptccm−1) during 82.2% of the waiting time. Compared to the park, the UFP concentration at the bus station in the canyon was found to be more responsive to changes in traffic volume, and the diffusion of UFP was more dependent on meteorological conditions, according to the results of grey relational analysis. However, near the park, there was no significant difference in UFP levels with and without the bus station. Based on our findings, we recommend that the department of urban planning and design consider creating a more open diffusion environment when setting up bus stops to reduce the risk of exposure to high level of UFP pollution.

An improved Prophet emergency traffic-flow prediction model

Emergencies are rare and random, but cause dramatic changes in traffic volumes whenever they occur. It is therefore very challenging to predict emergency traffic volumes accurately. The aim of this paper is to provide a practical methodology for predicting traffic volumes during emergencies; this is done using the Prophet model and by improving the event function of the model. The proposed approach isolates the event impacts through time series decomposition techniques; it allows the model to add points in time when traffic flow changes abruptly and to incorporate external factors useful to adapt to the specific background imposed by the emergency. The main data used in the paper were from the daily traffic volume data set published on the Luxembourg Open Data platform. These data were collected over a period from January 2017 to December 2021. The data set covers the period of impact of two emergencies. The proposed method and four comparator models were applied to the second emergency period. The results show that the proposed method can accurately predict unconventional changes caused by emergencies and, by way of a comparative analysis, that it has better prediction accuracy with real data than the other comparator models under the same attribute conditions.

Changes in motor traffic in London’s Low Traffic Neighbourhoods and boundary roads

This paper meta-analyses traffic data extracted from monitoring reports for 46 Low Traffic Neighbourhood schemes in 11 London boroughs introduced between May 2020 and May 2021. Schemes are controversial with still limited academic evidence on impacts. The analysis covers internal and boundary roads, looking at actual changes in motor traffic, and at what changes might have been expected based on background trends in London’s three ‘functional zones’ (Central, Inner, and Outer). All metrics show substantial relative declines in motor traffic on internal roads. For instance, a mean ‘pre-LTN’ traffic volume of 1,780 dropped to 930 ‘post-LTN’, against a small projected decline to 1,745 if background trends were followed. By contrast, the schemes are only marginally associated with change in traffic volume on boundary roads. While there are inevitable data limitations associated with the use of routine monitoring data, this research provides some support for the position that LTNs can form an effective part of wider strategies to reduce motor traffic and its associated disbenefits. Monitoring data should be publicly shared regionally and nationally to aid analysis.

Examining Seasonal Changes in Light-Vehicle Traffic Volume on Freeways Under Extreme Weather Conditions: A Combination of Temporal Statistical and Data Mining Non-Parametric Techniques

Today, with the increasing changes in weather patterns and the huge amount of data related to weather and traffic in different parts of the freeway network, the use of data mining methods to quantify the impact of weather on traffic flow is inevitable. The main objective of this study is to present a geostatistical method for computing and analyzing the effects of base and extreme cases of weather variables on light-vehicle traffic volumes on freeways, with an emphasis on temporal changes on different days of the week and between daytime and nighttime. In the proposed method for statistical analysis, the parametric test of two-way analysis of variance was used. In the following, with the development of a nonparametric method based on the classification and regression tree (CART) decision tree algorithm, the weather-related parameters with the greatest effect on traffic volumes were investigated separately for different seasons. For this purpose, nine years of statistics covering traffic and weather data for the studied freeway were analyzed. The computational results show that a fall in temperature of more than 35% on weekdays during the cold season and the parameters of horizontal visibility and rainfall during the day and temperature during the night in the spring cause a reduction of traffic volume of more than 45%. This study has shown that the combination of data-driven parametric and nonparametric data mining techniques is effective for traffic managers in planning and traffic control under extreme weather conditions. In this regard, an adverse weather conditions dynamic message sign (AWCDMS) framework was proposed as a practical way to warn drivers of adverse weather.

Model Development and Simulation for Predicting Proportional Changes in Traffic Volume as a Consequence of Natural Weather Hazards

Abstract This study used a model to calculate the proportional drop for every vehicle class based on 266 climate patterns consisting of seven temperature groups and varied snowfalls. The winter traffic models use weigh-in-motion (WIM) traffic collected on the commuter roadway for 5 years. The marginal impact and combined effect of meteorological conditions on the proportional decrease in winter traffic volume are evaluated. The predicted percentage decrease in traffic for all three vehicle classes increases as temperature decreases and snowfall increases. Mathematical functions are fitted for the decreased patterns for the considered vehicle type. Roadway authorities may utilize traffic percentage decrease to identify weather-related traffic changes when planning winter highway operation and maintenance.

Urban Mobility in the Postpandemic Stage: A Comprehensive Investigation of a Variety of Cities in China

The global outbreak of coronavirus disease 2019 (COVID-19) has affected the urban mobility of nations around the world. The pandemic may even have a potentially lasting impact on travel behaviors during the post-pandemic stage. China has basically stopped the spread of COVID-19 and reopened the economy, providing an unprecedented environment for investigating post-pandemic travel behaviors. This study conducts multiple investigations to show the changes in travel behaviors in the post-pandemic stage, on the basis of empirical travel data in a variety of cities in China. Specifically, this study demonstrates the changes in road network travel speed in 57 case cities and the changes in subway ridership in 26 case cities. Comprehensive comparisons can indicate the potential modal share in the post-pandemic stage. Further, this study conducts a case analysis of Beijing, where the city has experienced two waves of COVID-19. The variations in travel speed in the road network of Beijing at different stages of the pandemic help reveal the public’s responses towards the varying severity of the pandemic. Finally, a case study of the Yuhang district in Hangzhou is conducted to demonstrate the changes in traffic volume and vehicle travel distance amid the post-pandemic stage based on license plate recognition data. Results indicate a decline in subway trips in the post-pandemic stage among case cities. The vehicular traffic in cities with subways has recovered in peak hours on weekdays and has been even more congested than the pre-pandemic levels; whereas the vehicular traffic in cities without subways has not rebounded to pre-pandemic levels. This situation implies a potential modal shift from public transportation to private vehicular travel modes. Results also indicate that commuting traffic is sensitive to the severity of the pandemic. This may be because countermeasures, e.g., work-from-home and suspension of non-essential businesses, will be implemented if the pandemic restarts. The travel speed in non-peak hours and on non-workdays is higher than pre-pandemic levels, indicating that non-essential travel demand may be reduced and the public’s vigilance towards the pandemic may continue to the post-pandemic stage. These findings can help improve policymaking strategies in the post-pandemic new normal.

Concept of heterogeneity index for urban mixed traffic

• This paper conceptualizes a new measure to quantify the heterogeneity of mixed traffic. A proportion-based variant of COV of dynamic PCU values is proposed for this purpose. • This study develops a ‘Gaussian Process Regression’-based classified speed model which is further used to estimate the PCU of different vehicle categories and determine the ‘Heterogeneity Index’ of the mixed traffic stream. • The study proposes the heterogeneity levels using the K-medoids clustering technique along with Davies-Bouldin Index and Silhouette Index as validation measures. • Sensitivity analysis performed in the study shows the effect of traffic volume, traffic composition, and classified speed on the proposed ‘Heterogeneity Index’. Heterogeneity is one of those characteristics which differentiate traffic conditions of a developing country from other developed nations. The heterogeneity which represents the diversity among vehicle categories is suspected to have adverse influences on lane discipline, congestion potential, and road users’ safety. However, the influence of heterogeneity on the above-mentioned parameters has been only measured indirectly by considering traffic composition as an indicator of the prevailed heterogeneity-level. No direct relationship between the heterogeneity and other parameters has been established as there is no methodology available yet for quantifying the heterogeneity of mixed traffic. The present study addresses this problem and conceptualizes the ‘Heterogeneity Index’ (HI) to quantify the heterogeneity present in a mixed traffic stream. HI is conceived as a measure of the dispersion of Passenger Car Units (PCU) for different vehicle categories from its central value. A higher value of HI signifies more diverse vehicle categories present in the traffic stream. PCU of a vehicle category was estimated using the speed-based method and the individual speeds were predicted based on classified volumes using the Gaussian Process Regression model developed in this study. This paper also recommends several categorical levels for easy perception about the intensity of heterogeneity. Further, the sensitivity analysis explored the dynamic aspects of HI. Results showcased how the HI of a traffic stream may vary subject to the combined or the individual change in traffic volume, traffic composition and classified speeds. The outcomes of the study will be useful to estimate the intensity of heterogeneity that prevailed within a mixed traffic stream with varying traffic conditions.

Near-term impact of COVID-19 pandemic on seniors’ crash size and severity

Recent research revealed that COVID-19 pandemic was associated with noticeable changes in travel demand, traffic volumes, and traffic safety measures. Despite the reduction of traffic volumes across the US, several recent studies indicated that crash rates increased across different states during COVID-19 pandemic. Although some recent studies have focused on examining the changes in traffic conditions and crash rates before and during the pandemic, not enough research has been conducted to identify risk factors to crash severity. Even the limited research addressing the contributing factors to crash severity were focused on the pool category of drivers and no insight is available regarding older drivers, one of the most vulnerable groups to traffic collision and coronavirus. Moreover, these studies investigated the early impact of the COVID-19 pandemic mostly using up to three months of data. However, near-term and long-term effects of the COVID-19 pandemic are still unknown on traffic collisions. Therefore, this study aims to contribute to the literature by studying the near-term impact of the COVID-19 pandemic on crash size and severity among older drivers. To this end, a relatively large sample of crash data with senior drivers at fault was obtained and analyzed. To identify the main contributing factors affecting crash outcomes, Exploratory Factor Analysis was conducted on a high-dimension data set to identify potential latent factors which were validated through Confirmatory Factor Analysis. After that, Structural Equation Modeling technique was performed to examine the associations among the identified independent latent factors and the dependent variable. Additionally, SEM model identified the impact of the COVID-19 pandemic on seniors’ crash severity. The findings reveal that several latent variables were the significant predictors of crash severity of older drivers including “Driving maneuver & crash location”, “Road features and traffic control devices”, “Driver condition & behavior”, “Road geometric characteristics”, “Crash time and lighting”, and “Road class” latent factors. The binary variable of “Pandemic” was found to be as highly significant as the last four latent factors mentioned above. This means not only were older drivers more likely to be involved in higher crash size with higher severity level during the pandemic period, but also “Pandemic” was a risk factor to seniors as much as “Driver condition & behavior”, “Road geometric characteristics”, “Crash time & lighting”, and “Road class” factors. The results of this study provide useful insights that may improve road safety among senior drivers during pandemic periods like COVID-19.

A Novel Methodology for Predicting Roadway Deterioration in Iraq

The accurate prediction of roadway conditions is challenging for infrastructure services, especially when considering an increase in traffic volume. This is the first study conducted in Iraq that focuses on predicting roadway condition deterioration and its relation to yearly traffic volume, using surveying data collected between 2019 and 2021. The main purose of the conducted study was to inspect the accuracy, reliability, and ability of a combination of predictive techniques, this combination including Markovian Chains (MCs) and Artificial Neural Networks (ANNs), known as (MC-ANN), accurately to forecast mid-term to long-term (yearly) roadway condition. The principal findings of this research are as follows: a) MCs is a powerful method applied to predict future condition depending on previous one; b) ANNs modelling was performed that be able to produce a more reliable model of roadway condition based on selected road traffic volume change, climate circumstances and road age. The study reached a correlation coefficient of 0.94 between inspected and predicted roadway conditions using a valid collected dataset and a slight mean square error of 0.0195.