Traffic Policies Research Articles

Major Traffic Safety Reform and Road Traffic Injuries Among Low-Income New York Residents, 2009-2021.

Objectives. To evaluate the effects of a comprehensive traffic safety policy-New York City's (NYC's) 2014 Vision Zero-on the health of Medicaid enrollees. Methods. We conducted difference-in-differences analyses using individual-level New York Medicaid data to measure traffic injuries and expenditures from 2009 to 2021, comparing NYC to surrounding counties without traffic reforms (n = 65 585 568 person-years). Results. After Vision Zero, injury rates among NYC Medicaid enrollees diverged from those of surrounding counties, with a net impact of 77.5 fewer injuries per 100 000 person-years annually (95% confidence interval = -97.4, -57.6). We observed marked reductions in severe injuries (brain injury, hospitalizations) and savings of $90.8 million in Medicaid expenditures over the first 5 years. Effects were largest among Black residents. Impacts were reversed during the COVID-19 period. Conclusions. Vision Zero resulted in substantial protection for socioeconomically disadvantaged populations known to face heightened risk of injury, but the policy's effectiveness decreased during the pandemic period. Public Health Implications. Many cities have recently launched Vision Zero policies and others plan to do so. This research adds to the evidence on how and in what circumstances comprehensive traffic policies protect public health. (Am J Public Health. 2024;114(6):633-641. https://doi.org/10.2105/AJPH.2024.307617).

Analisis Model Pengambilan Keputusan Dalam Implementasi Kebijakan Ganjil Genap Di Jakarta Guna Mengurangi Kemacetan Dilihat Dari Teori Rasionalitas

DKI Jakarta faces significant traffic congestion due to the increasing population and number of motor vehicles. This research uses a qualitative approach through literature review to identify the factors causing congestion and to formulate effective solutions.The findings reveal that congestion is caused by high private vehicle usage, low use of public transportation, and limited road space. The proposed solutions in this study include improving public transportation infrastructure, implementing an odd-even traffic policy, and enforcing a 3 in 1 rule for cars. The analysis results show that implementing the odd-even policy is the most effective solution to reduce congestion. This policy has proven to reduce the volume of vehicles on the road and encourages the public to switch to public transport, significantly reducing exhaust emissions, improving air quality, increases the efficiency of road space usage, and lowers the traffic accident rate. The success of this solution requires active support and participation from the public. Therefore, the government needs to continuously involve the public in the decision-making process and provide adequate education about the impact and urgency of congestion issues. Collaboration between the government and the public is crucial to creating effective solutions to address congestion and enhance sustainable mobility in Jakarta.

Playing politics with traffic fines: Sheriff elections and political cycles in traffic fines revenue

AbstractThe political budget cycle theory has extensively documented how politicians manipulate policies during election years to gain an electoral advantage. This paper focuses on county sheriffs, crucial but often neglected local officials, and investigates their opportunistic political behavior during elections. Using a panel data set covering 57 California county governments over four election cycles, we find compelling evidence of traffic enforcement policy manipulation by county sheriffs during election years. Specifically, a county's per capita traffic fines revenue is 30% lower in the election than in nonelection years. The magnitude of the political cycle intensifies when an incumbent sheriff runs for reelection or an election is competitive. Our findings contribute to the political budget cycle theory and provide timely insights into the ongoing debate surrounding law enforcement reform and local governments’ increasing reliance on fines and fees revenue.

Impact of traffic policy on travel population exposure during heavy pollution episodes: A Chengdu case study

Previous studies on exposure have often omitted the impact of travelers' response to policies during air pollution, particularly for multi-modal traffic system. To address this gap, we investigate the population exposure of three modes (cars, buses, and metro) during morning rush hour in Chengdu, China, amidst the implementation of contingent traffic-related policies during heavy pollution episodes with elevated background pollutant concentrations of PM2.5. We introduce a trip-based average population exposure model to evaluate travel population exposure. This model allows to calculate the exposure for all modes, and assess its changes owing to modal shifts. Additionally, we propose a traffic-modal exposure coefficient to quantify PM2.5 exposure disparities among traffic modals. The results indicate that: the population exposure of total travelers experiences a growth effect, influenced not only by increased pollutant concentration but also by modal shift. Lastly, the inside-to-outside ratio, bus waiting time, and attractiveness of active traveler are discussed for more policy implications. This study not only offers a quantitative model to uncover potential overestimation of the effect of policies on reducing exposure if failure to consider modal shift, but also provides valuable insights for managing exposure growth to safeguard health of urban residents and promote sustainable development of cities.

Metode AHP Untuk Sistem Pendukung Keputusan Sekolah Sadar Lalu Lintas

Traffic conscious school covers various aspects that influence the effectiveness and accuracy of traffic education program evaluation. A structured and comprehensive approach is needed involving all stakeholders to update and adapt the traffic conscious school strategy to suit needs and developments. DSS is proposed to support traffic conscious school decisions using the website based AHP method. DSS was built adopting the XP Model with planning, design, coding and testing stages. Planning consists of functional and non functional requirements. The design contains application designs and AHP calculation designs with a CR value of 0,04 for 5 criteria 4 sub criteria, which means they are consistent. The highest priority value is 048 for the teacher and human environment criteria, while the lowest is for the object environment criteria. The test results show there is no difference between manual calculations and the proposed system. DSS can support stakeholder decisions to adopt traffic conscious school policies and strategies.

Spatiotemporal differentiation calendar for car and truck flow on expressways: A case study of Jiangsu, China

The transport objects and demands of cars and trucks differ, leading to different spatiotemporal characteristics in their flow patterns. Existing studies on expressway flow are mainly focused on the overall traffic, however, a decomposition analysis of cars and trucks is necessary to accurately explore traffic prediction, management, and dynamic toll policy. This study presents calendars and spatial distribution of car and truck flow based on toll fee data in Jiangsu Province, China. Additionally, the influencing factors are explored by using Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models. The results show that: (1) car flow follows a regular pattern of low from Monday–Thursday and high from Friday–Sunday, while high from Monday–Friday and low from Saturday–Sunday for truck flow. This law is not obvious in the total flow calendar. (2) The car flow predominantly accumulates in the 0–20 km range, while the truck flow is concentrated within the 20–40 km range. (3) The spatial distributions are stable for both car and truck flow, characterized by higher flows in the southern part of Jiangsu and lower in the northern region. Gaps in truck flow between cities are greater than those in car flow, narrowing during holidays/festivals than on weekdays or weekends. (4) private vehicle possession and travel time to Shanghai are the main influencing factors on car flow during different periods, the number of research institutions and the added value of tertiary industry are positively correlated on weekdays and weekends, respectively. Total retail sales of consumer goods is the main factor affecting truck flow during different periods, total import and export goods can be attributed to weekdays, while per capita disposable income affects weekends and festivals. Finally, the findings of this study offer insights for the optimal implementation of toll policies.

Implications of Singapore's Congestion Pricing Policy for China's Transportation Governance

With the advent of the post-epidemic era, traffic congestion in urbanized areas around the world is showing a serious trend. China, as the world's largest developing country, faces serious traffic problems during urbanization. In contrast, Singapore, as one of the Asian countries, has successfully implemented a series of traffic management policies, including congestion pricing. The main theme of this paper is to study the inspiration of Singapore's congestion pricing policy on China's transportation governance. This paper mainly analyzes the reasons for the success of Singapore's congestion pricing policy. At the same time, according to the current situation of traffic congestion in China and related research results, it summarizes the three aspects of congestion pricing in China to implement the proposal. First, congestion pricing policies should be implemented in parallel with other means of transportation management. Secondly, the congestion charging policy should be flexibly adjusted in the light of urban conditions. Finally, the congestion charging policy should focus on publicity and recognition by the public. Through this study, it is hoped that it can provide the right direction guidance and reference suggestions when the Chinese government considers the implementation of congestion pricing and similar policies in the future, and it is hoped to come up with more comprehensive and universal reference suggestions if further studies are conducted.

Analyzing The Influence of Social Media Posts on Government Policy Adoption in Papua City

In recent years, the use of social media has been very high among millennials and the middle class in Papua. This represents a new communication channel, especially in underdeveloped areas, where the political relations between citizens and the government are mostly conducted through conventional means such as demonstrations, dialogues, public hearings, submitting open letters to the government, putting up banners, and open complaint boxes. This article aims to analyze the role of social media in the process of public policy-making in the Province of Papua. The use of social media is very high among the millennial generation in Papua to respond to local government policies. However, its influence on policy change is still limited. Only traffic management policies have changed in response to criticisms on social media. This descriptive qualitative research was conducted through interviews and document analysis. The results show that social media plays a role in voicing public aspirations but has not yet influenced policy decision-making. There is a need for political will from the Papua Local Government to integrate citizens' aspirations into public policies. Further research could formulate a model of the relationship between citizens and the government through social media for more participatory public policies.

Predicting Road Traffic Collisions Using a Two-Layer Ensemble Machine Learning Algorithm

Road traffic collisions are among the world’s critical issues, causing many casualties, deaths, and economic losses, with a disproportionate burden falling on developing countries. Existing research has been conducted to analyze this situation using different approaches and techniques at different stretches and intersections. In this paper, we propose a two-layer ensemble machine learning (ML) technique to assess and predict road traffic collisions using data from a driving simulator. The first (base) layer integrates supervised learning techniques, namely k- Nearest Neighbors (k-NN), AdaBoost, Naive Bayes (NB), and Decision Trees (DT). The second layer predicts road collisions by combining the base layer outputs by employing the stacking ensemble method, using logistic regression as a meta-classifier. In addition, the synthetic minority oversampling technique (SMOTE) was performed to handle the data imbalance before training the model. To simplify the model, the particle swarm optimization (PSO) algorithm was used to select the most important features in our dataset. The proposed two-layer ensemble model had the best outcomes with an accuracy of 88%, an F1 score of 83%, and an AUC of 86% as compared with k-NN, DT, NB, and AdaBoost. The proposed two-layer ensemble model can be used in the future for theoretical as well as practical applications, such as road safety management for improving existing conditions of the road network and formulating traffic safety policies based on evidence.

Measuring acceptance of tradable credit scheme and its effect on behavioral intention through theory of planned behavior

Public support is crucial for the successful implementation of congestion charging schemes. The Tradable Credits Scheme (TCS), a novel concept built upon the cap-and-trade principle, is believed to possess qualities of effectiveness and fairness that should lead to greater public approval. This study investigates the level of acceptance of TCS among the public and its impact on the propensity to shift travel modes in the TCS framework. In this study, we used the Theory of Planned Behavior, encompassing components such as attitudes toward TCS, subjective norms and perceived behavioral control and assessed the perceived effectiveness and equity of TCS. Data were acquired through an online survey involving 544 participants in China. The findings indicate that public acceptance of TCS is significantly influenced by subjective norms and attitudes toward TCS. In turn, behavioral intention is indirectly shaped by these factors, with acceptance serving as a mediator. Perceived behavioral control exerts a direct influence on behavioral intention, while perceived effectiveness and equity positively impact both the acceptance of TCS and behavioral intention. Moreover, latent profile analysis was used to determine the influence of various factors on TCS acceptance, following which we categorized the participants into four distinct groups. The results show that age, city scale, income, holding a driving license or not, car ownership, necessity through congested areas, main transportation modes, and the frequency of using different travel modes all affect the distribution of individuals in support or rejection groups Notably, over one-third of participants express TCS acceptance, with most holding a neutral stance, indicative of TCS's potential for public acceptance with effective guidance and comprehensive explanation. In summary, this research provides novel insights into the multifaceted factors that influence individuals' acceptance of TCS and offers valuable assistance in promoting an innovative traffic demand management policy.

A Reliability-Based Network Equilibrium Model with Electric Vehicles and Gasoline Vehicles

With the popularity of electric vehicles, they have become an indispensable part of traffic flow on the road network. This paper presents a reliability-based network equilibrium model to realise the traffic flow pattern prediction on the road network with electric vehicles and gasoline vehicles, which incorporates travel time reliability, electric vehicles’ driving range and recharge requirement. The mathematical expression of reliable path travel time is derived, and the reliability-based network equilibrium model is formulated as a variational inequality problem. Then a multi-criterion labelling algorithm is proposed to solve the reliable shortest path problem, and a column-generation-based method of the successive average algorithm is proposed to solve the reliability-based network equilibrium model. The applicability and efficiency of the proposed model and algorithm are verified on the Nguyen-Dupuis network and the real road network of Sioux Falls City. The proposed model and algorithm can be extended to other road networks and help traffic managers analyse traffic conditions and make sustainable traffic policies.

A data‐driven adaptive geospatial hotspot detection approach in smart cities

AbstractHotspot detection from geo‐referenced urban data is critical for smart city research, such as traffic management and policy making. However, the classical clustering or classification approach for hotspot detection mainly aims at identifying “hotspot areas” rather than specific points, and the setting of global parameters such as search bandwidth can lead to inaccurate results when processing multi‐density urban data. In this article, a data‐driven adaptive hotspot detection (AHD) approach based on kernel density analysis is proposed and applied to various spatial objects. The adaptive search bandwidth is automatically calculated depending on the local density. Window detection is used to extract the specific hotspots in AHD, thus realizing a small‐scale characterization of urban hotspots. Through the trajectory data of Harbin City taxis and New York City crime data, Geo‐information Tupu is used to analyze the obtained specific hotspots and verify the effectiveness of AHD, providing new ideas for further research.

Energy-efficient Techniques in SDN: Software, Hardware, and Hybrid Approaches

In recent times, software-defined networking (SDN) has garnered significant attention due to its approach of separating the control plane from the data plane. This abstraction allows managers to have a centralized perspective on the network, which in turn enables them to control traffic flow and routing policies dynamically without configuring individual devices. The increased adoption of SDN has resulted in the need to optimize energy consumption without compromising network performance. Creating an energy-efficient solution presents a challenge due to the intricate process of finding the right equilibrium between network performance and energy conservation. This article examines various techniques for achieving energy efficiency in SDN and classifies them into three categories – software-based, hardware-based, and hybrid approach. These categories are further divided into their subcategory. Furthermore, a comprehensive exploration will be conducted to delve into the benefits and drawbacks of each energy efficiency approach, followed by an in-depth comparative analysis. The article also discusses the research issues and challenges associated with each technique. Finally, the paper provides future prospects for investigating energy efficiency approaches in SDN, which are expected to yield substantial benefits for upcoming researchers in this domain.

Changing Regularity of the Interaction Effects of Multi-Scale Factors on Drivers’ Merging Behaviors in the Highway Work Zone

This study aimed to explore the impacting mechanism of macro- and micro-factors and multi-scale synergy on drivers’ merging behaviors in the highway work zone, and then to facilitate future merging prediction research. The merging behavior, requiring drivers to detect adjacent vehicles and real-time traffic conditions simultaneously, is a complex cognitive process. Previous studies have mainly focused on the stable impacts of limited factors on merging probability, but ignored the varying states and drivers’ performance. Status-changing positions in the whole merging process were first identified by constructing and analyzing the relationship between running speed and distance from the construction area. Subsequently, the interaction analysis was conducted among the multi-scale traffic factors, utilizing optimized logistic models and interaction estimates, thus establishing macro- and micro-factor connections. Besides, the marginal effect was calculated to analyze the fluctuation degree of these connections. Finally, a multilevel identification framework was proposed, whose effectiveness and practicality were validated using 744 naturalistic vehicular trajectories from a real highway work area. At different positions, drivers are affected by various factors to varying degrees. While approaching the construction area, drivers become more passive, thus trying to avoid rear-ending the lead vehicle but ignoring the lag vehicle. Besides, traffic volume also affects drivers’ merging decisions by confusing their cognition toward the time headway. This research indicates that dynamic interaction effects between multi-scale factors could provide far-reaching benefits for lane-changing prediction. The findings provide a basis for formulating traffic management policies and constructing driving assistance systems.

Analysing the Environmental and Social Impacts of a Novel User‐Based Transit Signal Priority Strategy in a Connected Vehicle Environment

Transit signal priority (TSP) is a traffic control strategy aiming at prioritising public transit vehicles at signalised intersections. The emergence of connected vehicles (CVs) provides the opportunity to enhance TSP operation, mitigating challenges such as the negative impact on nontransit users and the management of conflicting priority requests. Furthermore, traffic control policies produce environmental impacts, whilst TSP strategies are typically evaluated based on common traffic flow indicators, such as average vehicle speed, delay and/or the number of stops. In light of the recent progress made in CV technology, we propose and assess two user‐based TSP strategies. The first approach aims to minimise total user delay at a signalised intersection, whilst the second considers both reducing bus schedule delay and total user delay. We also measure the environmental effects of these TSP strategies. A microscopic simulation environment is used to compare the proposed methods’ performance against a conventional TSP ring‐and‐barrier controller in a case study involving two adjacent signalised intersections in Helsinki, Finland. The findings indicate that implementing the proposed strategies effectively enhances TSP performance whilst also lowering adverse environmental impacts.

IMTRAC: Image-Based Modeling for Transformative Traffic Control via Foundation Models

This paper introduces the Image-based Modeling for Transformative Traffic Control (IMTRAC) framework, which revolutionizes urban traffic management by integrating image-based representations, foundation models, and dynamic signaling. It transitions from textual to visual data analysis, transforming complex traffic scenes into quantifiable insights. The image-based representation is then analyzed by foundation models to infer refined and effective traffic control policies. Our experimental results show marked improvements in traffic flow and reductions in waiting times, highlighting IMTRAC’s ability to outperform traditional control methods. The proposed IMTRAC promises a significant leap towards smarter, efficient urban traffic systems, leveraging the synergy of advanced imaging techniques and multi-modal foundation models to enhance control decisions.

Urban freight assessment as an instrument to improve urban policies in the city of Santo André, state of São Paulo, Brazil

This study evaluates urban freight movement in two selected areas of Santo André, a mid-sized city in the Metropolitan Region of São Paulo, in Brazil. The research method integrates Brazilian databases into the Freturb model and, which is combined to truck volume counting to analyze the urban freight behavior and how the land use patterns affect it. The study findings indicate that urban freight movements and the types of vehicles vary significantly between the two urban areas due to the different economic sectors and land use. Thus, by considering the characteristics of urban freight and land use together, policymakers and urban planners can develop more effective urban instruments for each area. Moreover, when truck volumes, from field work truck counting, and high cargo movement density, calculated from Freturb modelling, are mapped and show a close position, it is possible to conclude that the trucks are coming to or leaving from establishments in the city. However, when there is no proximity, trucks may be just passing through the city. This method may support the definition of new truck counting points with greater precision, as inputs for land use, traffic and logistics policies in dense urban areas.

Investigation of fatal traumatic head injuries.

Traumatic head injuries (THIs) are one of the major causes of death in forensic cases. The aim of this study was to investigate the characteristics of patients with fatal THIs. In this study, a total of 311 patients with fatal THIs, who underwent postmortem examinations and/or autopsies, were retrospectively analyzed. Cases were evaluated based on sex, age group, incident origin, cause of the incident, presence of skull fracture, type of fractured bone (if any), fracture localization and pattern, presence and type of intracranial lesion (if any), and cause of death. Out of the patients, 242 (77.8%) were male and 69 (22.2%) were female. Accidents accounted for 235 (75.6%) of the incidents, with in-vehicle traffic accidents causing 117 (37.6%). In 221 cases (71.1%), intracranial lesions and skull fractures were observed together. The most common fractures were base fractures (171 cases) and temporal bone fractures (153 cases). The rate of intracranial hemorrhage was lower in the adult age group (69.7%) compared to the older age group (92.6%). The results obtained in this study indicate that the cause of the incident, type of fracture, presence of skull base fracture, and multiple skull fractures increase the likelihood of fatalities. The occurrence of skull fractures reduces intracranial pressure, thereby decreasing the incidence of intracranial lesions. The development and effective enforcement of road traffic safety policies and regulations will reduce the incidence of fatalities.

Quantifying Impacts of Local Traffic Policies on PM Concentrations Using Low Cost Sensors in Berlin

Quantifying Impacts of Local Traffic Policies on PM Concentrations Using Low Cost Sensors in Berlin

Simulating the performance of HOV lanes for optimal urban traffic management

Traffic congestion is a significant problem in crowded cities, resulting in increased travel times, air pollution, and reduced productivity. High-occupancy vehicle (HOV) lanes have emerged as a promising solution to tackle these issues. HOV lanes are reserved for vehicles carrying multiple passengers, incentivizing shared travel and reducing the number of low-occupancy vehicles on the road like school buses. By reducing the overall number of cars on the road, HOV lanes can significantly alleviate traffic congestion, improve travel times, and reduce air pollution. The effectiveness of HOV lanes depends on the degree of demand for shared travel and the availability of incentives, such as employer-provided benefits or toll exemptions. Additionally, the design and implementation of HOV lanes must be carefully planned to ensure that they do not negatively impact other road users. Tehran, as one of the largest and most populous cities in Asia, is a major destination for daily travel by workers. The Tehran-Karaj freeway serves as the primary and busiest entrance to Tehran city, experiencing heavy traffic every day in the morning towards Tehran and in the evening towards Karaj. In this study, the traffic conditions of this freeway were simulated in the existing state and with the presence of HOV lanes, and the resulting traffic parameters were compared and analyzed under different traffic scenarios. The findings of this research can offer valuable insights for decision-makers and traffic policy makers.