Traffic Policies Research Articles

Machine learning approach on road accidents analysis in Calabarzon, Philippines: an input to road safety management

This research was conducted to help the traffic policy makers and general public in preventing road incidents using the collected traffic accident dataset between the years 2016 and 2019. Data mining using classification algorithm was utilized to develop a predictive model for predicting occurrences of traffic accidents. Classification algorithms such as decision tree, k-nn, naïve bayes and neural network have been compared in identifying better classification capability in classifying stage of felony. Neural network shows a very promising result in classifying road accident with a total accuracy result of 87.63%. Nonetheless, k-nn and naïve bayes both acquired a higher than 80% accuracy which shows that this classification algorithms were also good in predicting road accidents. Moreover, public vehicle is more prone in accident rather than private vehicle in both stage of felony and accident may occur between or on 3:00pm and 6:00pm.

Impacts of COVID-19 Traffic Control Policy on Population Flows in Changsha

To investigate the impact of COVID-19 traffic control policies on population flow in Changsha, this paper divided the prevention and traffic control policies into different stages corresponding to the real-time epidemic situation in Changsha. Based on Baidu migration big data, the difference- in- difference model was used to identify different stages of traffic prevention and control policies and quantify the effect of prevention. With the traffic control policy implemented during COVID-19, the average inflow intensity of Changsha City decreased by 83.68%, the average outflow intensity decreased by 69.24% and the internal travel intensity respectively, decreased by 59.74%. After the end of the traffic control policies, the population flow intensity of Changsha City gradually rebounded, and the urban internal travel intensity basically recovered to the same level as in 2019. The results indicated the effectiveness of the traffic control policies on the limitation of population flow and epidemic spread. The results also provide reference for making effective prevention and control policies for the normalized COVID-19 epidemic situations. Copyright © 2021 by Science Press.

To what extent the traffic restriction policies applied in Barcelona city can improve its air quality?

Barcelona city (Spain) is applying a series of traffic restriction measures that aim at renewing and reducing the amount of circulating vehicles to improve air quality. The measures include changes in the built environment to reduce private vehicle space in specific areas through the so-called “superblocks” and tactical urban planning actions, along with the implementation of a city-wide Low Emission Zone (LEZ) that restricts the entry of the most polluting vehicles to the city. Our study quantifies the impact of these measures in the greater area of Barcelona combining a coupled macroscopic traffic and pollutant emission model with a multi-scale air quality model. Our modelling system allows estimating the effect of different traffic restrictions upon traffic and the associated emissions and air quality levels at a very high resolution (20 m). The measures were evaluated both individually and collectively to assess both their relative and overall impact upon emissions and air quality. We show that in the absence of traffic demand reductions, the application of isolated measures that reduce private vehicle space, either through superblocks or tactical urban planning, have no overall emission impacts; only localized street-level NOx positive and negative changes (±17%) are found due to traffic re-routing and the generation of new bottlenecks. It is only when these measures are combined with optimistic fleet renewal as a result of the LEZ implementation and demand reductions, that relevant global emission reductions in NOx are obtained (−13% and −30%, respectively) with estimated NO2 reductions of −36% and −23% at the two traffic air quality monitoring stations. Despite the potential improvements, our simulations suggest that current measures are insufficient to comply with EU air quality standards and that further traffic restriction policies to reduce traffic demand are needed.

INVESTMENT ARTIFICIAL INTELLIGENCE IN DECISION-MAKING PROCESSES IN THE JORDANIAN MINISTRY OF INTERIOR

This research paper is part of a larger study on Jordan's artificial intelligence strategy and its role in changing government service delivery mechanisms, leading to a comprehensive data analysis using the most up-to-date artificial intelligence techniques and their applications in various fields of work The purpose of the study was to discuss Jordan's decision-making process and the application of artificial intelligence. The issue was presented in two demands using the descriptive-analytical approach. The conversations revealed that Jordan's artificial intelligence policy aims to reduce accidents and operational expenses in various key areas of the country, including transportation. Reduce the proportion of chronic and serious diseases in the health sector. Facilities management and smart consumption in the energy sector. The education sector, by lowering costs and encouraging the desire to learn, and the technology sector, by improving the rate of production and assisting in public investment. By increasing the proportion of forestation and planting appropriate plants, the environment sector will benefit. The sector of Transportation Develops preventative techniques, such as the ability to foresee accidents and traffic congestion, as well as better traffic policies. The presence of infrastructure and knowledge in the success of artificial intelligence recruitment in Jordan's Ministry of Interior.

An Improved Particle Swarm Optimization for Prediction of Accident Severity

In recent time, road traffic accidents are increased day by day due to incremental growth in number of vehicles. Traffic accidents are also the crucial and important issue for achieving the sustainable transportation development. One of the tasks of sustainable transportation is to reduce the number of accidents and also design the traffic assessment policy. Many researchers consider the traffic accident issue of sustainable transportation and developed prediction models for measuring severity of accidents. But the accuracy of accident severity is one of the major issues. In this work, an attempt is made to improve the accuracy of accident severity. To achieve the same, a particle swarm optimization-based algorithm is applied for evaluating the accident severity. Prior to implement the PSO, two modification are incorporated into PSO algorithm, called improved PSO. These modifications can be described as mutation operator and trail candidate generation. The performance of improved PSO is examined over accident traffic severity dataset and results are evaluated using accuracy, recall@5 and precision@5 metrics. Several existing techniques are considered for comparing the results of IPSO algorithm. It is revealed that IPSO achieves more accurate results among all techniques.

Mobility in post-pandemic economic reopening under social distancing guidelines: Congestion, emissions, and contact exposure in public transit.

COVID-19 has raised new challenges for transportation in the post-pandemic era. The social distancing requirement, with the aim of reducing contact risk in public transit, could exacerbate traffic congestion and emissions. We propose a simulation tool to evaluate the trade-offs between traffic congestion, emissions, and policies impacting travel behavior to mitigate the spread of COVID-19 including social distancing and working from home. Open-source agent-based simulation models are used to evaluate the transportation system usage for the case study of New York City. A Post Processing Software for Air Quality (PPS-AQ) estimation is used to evaluate the air quality impacts. Finally, system-wide contact exposure on the subway is estimated from the traffic simulation output. The social distancing requirement in public transit is found to be effective in reducing contact exposure, but it has negative congestion and emission impacts on Manhattan and neighborhoods at transit and commercial hubs. While telework can reduce congestion and emissions citywide, in Manhattan the negative impacts are higher due to behavioral inertia and social distancing. The findings suggest that contact exposure to COVID-19 on subways is relatively low, especially if social distancing practices are followed. The proposed integrated traffic simulation models and air quality estimation model can help policymakers evaluate the impact of policies on traffic congestion and emissions as well as identifying hot spots, both temporally and spatially.

Impact of Social Media on Travel Behaviors during the COVID-19 Pandemic: Evidence from New York City.

During the outbreak of COVID-19, people's reliance on social media for pandemic-related information exchange, daily communications, and online professional interactions increased because of self-isolation and lockdown implementation. Most of the published research addresses the performance of nonpharmaceutical interventions (NPIs) and measures on the issues impacted by COVID-19, such as health, education, and public safety; however, not much is known about the interplay between social media use and travel behaviors. This study aims to determine the effect of social media on human mobility before and after the COVID-19 outbreak, and its impact on personal vehicle and public transit use in New York City (NYC). Apple mobility trends and Twitter data are used as two data sources. The results indicate that Twitter volume and mobility trend correlations are negative for both driving and transit categories in general, especially at the beginning of the COVID-19 outbreak in NYC. A significant time lag (13 days) between the online communication rise and mobility drop can be observed, thereby providing evidence of social networks taking quicker reactions to the pandemic than the transportation system. In addition, social media and government policies had different impacts on vehicular traffic and public transit ridership during the pandemic with varied performance. This study provides insights on the complex influence of both anti-pandemic measures and user-generated content, namely social media, on people's travel decisions during pandemics. The empirical evidence can help decision-makers formulate timely emergency responses, prepare targeted traffic intervention policies, and conduct risk management in similar outbreaks in the future.

Developing a Traffic Model to Estimate Vehicle Emissions: An Application in Seoul, Korea

In this study, a traffic demand model was created based on a simulation network, and another model was built to calculate exhaust-gas emissions generated by vehicles based on the emission function. Subsequently, emissions for three scenarios were analyzed based on the traffic restriction policy according to the vehicle grading system implemented in Seoul. According to the results of the analysis, emission reduction under the vehicle restriction policy was the highest among passenger cars in the low-speed range, while the emissions of cargo trucks in the high-speed range were found to be high. The emissions showed a high ratio of carbon monoxide and nitrogen oxides, and high emissions were generated from liquefied petroleum gas and diesel vehicles. Furthermore, the effects of vehicle restriction policy were confirmed to reduce emissions from diesel and other vehicle types. Using the established model, we were able to confirm that the vehicle restriction policy contributed to the improvement of air quality. Furthermore, the diesel vehicle restriction policy also had an impact on reducing the emissions of vehicle types other than those using diesel.

Spatial–Temporal Pattern and Influence Factors of Land Used for Transportation at the County Level since the Implementation of the Reform and Opening-Up Policy in China

In this paper, we study the characteristics of the spatial–temporal pattern of land used for transportation at the county level since the implementation of the reform and opening-up policy in China and discuss the factors that influence the spatial differences between lands used for transportation in order to provide a reference for the formulation of traffic policies. The authors used ArcGIS spatial analysis, an ordinary least squares (OLS) regression model, and a geographic detector model based on the data of the transportation network at the county level in China from 1978 to 2018. We obtained the following results: (1) The land used for transportation at the county level in China is divided by the Hu Huanyong Line, which is characterized by spatial variation, where the southeastern region is higher than the northwestern region. (2) Counties with a high proportion of land used for transportation show obvious changes, characterized by the transformation from the “corridor” zonal distribution of arteries to the “diamond” group distribution of major city clusters, reducing the gap in land used for transportation at the county level in China. (3) The level of industrialization, per capita gross regional product (PGRP), and ratio of the non-agricultural working population all have an incentivizing impact on the increase in land used for transportation at the county level in China. We conclude that the land used for transportation at the county level in China is jointly decided by the economy, industry, and population. Therefore, we believe that it is necessary to promote fast economic growth, the upgrading of industrial structures, and population density to achieve the balanced development of land used for transportation at the county level in China.

Towards a systematic computational framework for modeling multi-agent decision-making at micro level for smart vehicles in a smart world

We propose a multi-agent based computational framework for modeling decision-making and strategic interaction at micro level for smart vehicles in a smart world. The concepts of Markov game and best response dynamics are heavily leveraged. Our aim is to make the framework conceptually sound and computationally practical for a range of realistic applications, including micro path planning for autonomous vehicles. To this end, we first convert the would-be stochastic game problem into a closely related deterministic one by introducing risk premium in the utility function for each individual agent. We show how the sub-game perfect Nash equilibrium of the simplified deterministic game can be solved by an algorithm based on best response dynamics. In order to better model human driving behaviors with bounded rationality, we seek to further simplify the solution concept by replacing the Nash equilibrium condition with a heuristic and adaptive optimization with finite look-ahead anticipation. In addition, the algorithm corresponding to the new solution concept drastically improves the computational efficiency. To demonstrate how our approach can be applied to realistic traffic settings, we conduct a simulation experiment: to derive merging and yielding behaviors on a double-lane highway with an unexpected barrier. Despite assumption differences involved in the two solution concepts, the derived numerical solutions show that the endogenized driving behaviors are very similar. We also briefly comment on how the proposed framework can be further extended in a number of directions in our forthcoming work, such as behavioral calibration using real traffic video data, and computational mechanism design for traffic policy optimization.

Effects of inhomogeneous ground-level pollutant sources under different wind directions

Effects of source inhomogeneity on pollutant dispersion from a cubic building array are investigated as a function of the external wind direction. Using building-resolving large-eddy simulation, it is found that the results depend strongly on the source location and source uniformity inside a near-field region defined by a radius of homogenisation (RAD) based on the spatial autocorrelation of the pollutant concentration. The sensitivity of the RAD to the source location changes abruptly around 30° and is greatly reduced for wind angles between 30 and 45°, in agreement with velocity statistics and the mean horizontal streamlines. The optimal source allocation, which is a proxy for emissions from time-dependent traffic, also changes around 30°. This work clarifies the relationship between inhomogeneous velocity and pollutant statistics and may be applied to the formulation of traffic control policy.

دور الذكاء الاصطناعي في عملية اتخاذ القرارات في وزارة الداخلية بدولة الإمارات العربية المتحدة

This research paper is part of an extensive study conducted by the researcher on the UAE strategy for artificial intelligence, and its role in changing the mechanism of government services, leading to a complete data analysis by 2031, according to the latest techniques and tools of artificial intelligence and their application in various fields of work efficiently. The problems of smart devices lie in the bubble filter phenomenon, which causes a lack of elasticity, increased systemic fragility, which can have serious and unexpected systemic effects. The paper objectives were; to discuss the decision-making process and the employment of artificial intelligence in the United Arab Emirates. Through the Descriptive Analytical method, the topic was presented in two sections. The discussions showed that the UAE's strategy for artificial intelligence targets several vital sectors in the country, including the transportation sector, by reducing accidents and operational costs. The health sector by reducing the proportion of chronic and dangerous diseases. The space sector conducts accurate experiments and reduces costly errors. Energy sector through utility management and smart consumption. The water sector by conducting accurate analysis and studies to save resources. The technology sector by raising the rate of production and helping in public spending, the education sector by reducing costs and increasing the desire to learn. The environment sector by increasing the proportion of afforestation and planting suitable plants. Traffic Sector Develop preventive mechanisms such as predicting accidents and traffic congestion, and setting more effective traffic policies. With the presence of the infrastructure and knowledge in the successful recruitment of artificial intelligence in the Ministry of Interior in the United Arab Emirates.

Exploring the Citywide Human Mobility Patterns of Taxi Trips through a Topic-Modeling Analysis

Understanding human mobility patterns is of great importance for a wide range of applications from social networks to transportation planning. Toward this end, the spatial-temporal information of a large-scale dataset of taxi trips was collected via GPS, from March 10 to 23, 2014, in Beijing. The data contain trips generated by a great portion of taxi vehicles citywide. We revealed that the geographic displacement of those trips follows the power law distribution and the corresponding travel time follows a mixture of the exponential and power law distribution. To identify human mobility patterns, a topic model with the latent Dirichlet allocation (LDA) algorithm was proposed to infer the sixty-five key topics. By measuring the variation of trip displacement over time, we find that the travel distance in the morning rush hour is much shorter than that in the other time. As for daily patterns, it shows that taxi mobility presents weekly regularity both on weekdays and on weekends. Among different days in the same week, mobility patterns on Tuesday and Wednesday are quite similar. By quantifying the trip distance along time, we find that Topic 44 exhibits dominant patterns, which means distance less than 10 km is predominant no matter what time in a day. The findings could be references for travelers to arrange trips and policymakers to formulate sound traffic management policies.

Heatwave impacts on traffic accidents by time-of-day and age of casualties in five urban areas in South Korea

We analyzed the relationship between the temperature and traffic accidents in Seoul-Incheon, Busan, Daegu, Daejeon, and Gwangju by the time-of-day (06:00 to 22:00, divided into segments of 4 h) and age of casualties between 2012 and 2017 for the summer season (June to August). A generalized additive model and meta-analysis were employed to analyze this relationship. We found that the threshold temperatures was observed to be approximately 30 °C. Above this temperature, traffic accidents increased in four urban areas, except Busan, which is a popular tourist location. In total, traffic accidents increased by approximately 0.59% (95% confidence interval of 0.41–0.75) per 1 °C increase in the daily maximum temperature, with substantial differences between the different areas, ranging from 0.12% (CI = −0.26–0.50) in Busan to 1.08% (CI = 0.45–1.71) in Gwangju. The morning and evening hours showed a greater increase in traffic accidents than other timeframes. The increase in traffic accidents for young casualties was statistically significant at all times, and that for elderly casualties was observed at 10:00–14:00 and 18:00–22:00. The results of this study could provide information for developing customized traffic accident reduction policies considering time-of-day, age of casualties, and type of city.

Epidemiological characteristics of road traffic injuries accepted by Shenzhen Emergency Center from 2010 to 2018

Objective To investigate the current situation of road traffic injuries (RTI) among the population in Shenzhen from 2010 to 2018, and to provide references and suggestions for improving road traffic safety related policies and traffic injures prevention in Shenzhen. Methods Retrospective analysis was performed on the RTI data accepted by the Emergency Comprehensive Information Query Platform of Shenzhen Emergency Center from 2010 to 2018, and statistics were made on the overall situation, population characteristics, time, regional distribution and severity of RTI in Shenzhen. Results From 2010 to 2018, there were 79 369 cases of RTI in Shenzhen, and the ratio of injuries to instant deaths was 79.66∶1. The incidence of RTI in Shenzhen Emergency Center was generally decreasing (χtrend2=8.200, P=0.004), and the on-site death rate was also showed a decreasing (χtrend2=4.604, P=0.032) trend. The incidence rate of male was 478.90 per 100 000, which was higher than that of female (223.92 per 100 000, χ2=92.823, P Conclusion According to the epidemiological characters from 2010 to 2018, the key points to preventing and controlling RTI would be: improving the road traffic management system; improving the traffic safety awareness of the key crowds; strengthening the roads regulation during the rush hours; deploying the emergency resources more rationally; shorting the emergency response time; improving the first aid system and service mode; and developing scientific precautionary measures.

Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China

Few study has revealed spatial transmission characteristics of COVID-19 in Wuhan, China. We aimed to analyze the spatiotemporal spread of COVID-19 in Wuhan and its influence factors. Information of 32,682 COVID-19 cases reported through March 18 were extracted from the national infectious disease surveillance system. Geographic information system methods were applied to analysis transmission of COVID-19 and its influence factors in different periods. We found decrease in effective reproduction number (Rt) and COVID-19 related indicators through taking a series of effective public health measures including restricting traffic, centralized quarantine and strict stay-at home policy. The distribution of COVID-19 cases number in Wuhan showed obvious global aggregation and local aggregation. In addition, the analysis at streets-level suggested population density and the number of hospitals were associated with COVID-19 cases number. The epidemic situation showed obvious global and local spatial aggregations. High population density with larger number of hospitals may account for the aggregations. The epidemic in Wuhan was under control in a short time after strong quarantine measures and restrictions on movement of residents were implanted.

Freight operations in city centers: A land use conflict in urban planning

Municipalities in Colombia have insubstantially developed methods and technical support to diagnose and plan the supply chain in consolidated urban spaces. This situation becomes critical due to strong illegal occupation and deterioration of public space, overcrowding by vulnerable populations, informal commerce and common delinquency. This research paper aims to present a diagnosis of loading and unloading operations in Cali’s city-center and, subsequently, to study some improvement strategies considering stakeholders’ opinion. A combination of methodologies permitted obtaining relevant results that shed light on possible innovative actions to reduce land use conflict in city centers of medium sized cities in emerging countries. The results revealed that the freight parking offer was very scarce and concentrated on bay infrastructure and on-street parking, increasing waiting times and congestion. Parking was not controlled, and illegal practices were present throughout the day, including pedestrian path occupation. A combined set of strategies, such as cargo cycle operation and mini-hubs, could raise the parking offer and adapt to different demand conditions in terms of schedules and vehicles sizes. Cooperation between private stakeholders and the strength of institutional structure is essential to harmonize urban freight evaluations and operations with sustainable mobility policies in city centers. Some strategies require a strong set of public incentives, including tax benefits and logistic land use implementation with price controls. City Centers requires loading and unloading zones and logistic land, besides traffic management policies. Logistic land use is disappearing in central urban zones, which is contradictory and is producing a perverse effect. This is an analysis that reveals the importance of planning mixed land uses that include logistics and different vocations of public space.

Analyzing the Coordination Relationship between Urban Built Environment and Traffic Level

The coordination relationship between urban built environment and transport system is an indispensable field in the study of urban planning. Recent research efforts in built environment and transport system have focused on the effects of built environment on travel behaviors, such as car ownership, choice of travel mode, and travel frequency. These travel behaviors will affect the traffic level. However, research studies on direct assessments of links between built environment and traffic level are still limited. This paper aims to fill this gap by modeling with data envelopment analysis based on Point of Interest (POI) data and floating car data collected in Jinan, China. It is found that the coordination relationship between built environment and traffic level is poor in Jinan. With regard to the built environment input index, the distance from the city center has the greatest influence on the coordination relationship. And for the built environment output index, bus stop influences the coordination relationship most significantly. This research can support the provision of quantitative basis for the formulation of governance priorities for traffic governance policies.

A numerical study of local traffic volume and air quality within urban street canyons

Although pollutant sources are often assumed to be spatially uniform, traffic in real cities may vary significantly in space. Consequently the local air quality within a street may not be determined solely by the traffic volume of the street. Using building-resolving large-eddy simulation, the relationship between traffic volume and air quality is investigated in the context of two idealised problems: (i) the influence of pollutants emitted from a main road on the surrounding side streets and (ii) the pedestrianisation of a central thoroughfare. It is shown that the spatial variation of traffic volume is of crucial importance within a near-field region defined by a radius of homogenisation (RAD). Furthermore, the actual impact depends strongly on the wind direction. Hence the benefits of pedestrianisation may be limited: for example, after removing 100% of the traffic along a street in a central business district, the annual-averaged local concentration decreases by ~30% when the urban background is neglected. The impact may be significantly lower when the background concentration is considered. This work is relevant to the formulation of effective traffic control policy and the improved understanding of spatially inhomogeneous pollutant sources.

Influence of transportation network on transmission heterogeneity of COVID-19 in China

In this paper, we propose a novel approach to model spatial heterogeneity for epidemic spreading, which combines the relevance of transport proximity in human movement and the excellent estimation accuracy of deep neural network. We apply this model to investigate the effects of various transportation networks on the heterogeneous propagation of COVID-19 in China. We further apply it to predict the development of COVID-19 in China in two scenarios, i.e., i) assuming that different types of traffic restriction policies are conducted and ii) assuming that the epicenter of the COVID-19 outbreak is in Beijing, so as to illustrate the potential usage of the model in generating various policy insights to help the containment of the further spread of COVID-19. We find that the most effective way to prevent the coronavirus from spreading quickly and extensively is to control the routes linked to the epicenter at the beginning of the pandemic. But if the virus has been widely spread, setting restrictions on hub cities would be much more efficient than imposing the same travel ban across the whole country. We also show that a comprehensive consideration of the epicenter location is necessary for disease control.