Traffic Impact Research Articles

Mitigating traffic congestion induced by transportation network companies: A policy analysis

This paper analyzes and evaluates several policies aiming to mitigate the congestion effect a Transportation Network Company (TNC) brings to bear on an idealized city that contains a dense central core surrounded by a larger periphery. The TNC offers both solo and pooling e-hail services to the users of public transport. We develop a spatial market equilibrium model over two building blocks: an aggregate congestion model that describes the traffic impact of TNC operations on all travelers in the city, including private motorists, and a matching model that estimates the TNC’s level of service based on the interactions between riders and TNC drivers. Based on the equilibrium model, we formulate and solve the optimal pricing problem, in which the TNC seeks to optimize its profit or social welfare subject to regulatory costs and/or constraints. Three congestion mitigation policies are implemented in this study: (i) a trip-based policy that charges a congestion fee on each solo trip starting or ending in the city center; (ii) a cordon-based policy that charges TNC vehicles entering the city center with zero or one passenger; and (iii) a cruising cap policy that requires the TNC to maintain the fleet utilization ratio in the city center above a threshold. Based on a case study of Chicago, we find TNC operations may have a significant congestion effect. Failing to anticipate this effect in the pricing problem leads to sub-optimal decisions that worsen traffic congestion and hurt the TNC’s profitability. Of the three policies, the trip-based policy delivers the best performance. It reduces traffic congestion modestly, keeps the TNC’s level of service almost intact, and improves overall social welfare substantially. The cruising cap policy benefits private motorists, thanks to the extra congestion relief it brings about. However, because other stakeholders together suffer a much greater loss, its net impact on social welfare is negative. Paradoxically, the policy could worsen the very traffic conditions in the city center that it is designed to improve.

The deceitful Connected and Autonomous Vehicle: Defining the concept, contextualising its dimensions and proposing mitigation policies

The Connected and Autonomous Vehicle (CAV) is an emerging mobility technology that may hold a paradigm-changing potential for the future of transport policy and planning. Despite a wealth of likely benefits that have made their eventual launch inescapable, CAVs may also be a source of unprecedented disruption for tomorrow's travel eco-systems because of their vulnerability to cyber-threats, hacking and misinformation. CAVs manipulated by users, traffic controllers or third parties may act in deceitful ways. This scene-setting work introduces the deceitful CAV, a vehicle that operates in a deceitful manner towards routing and control functionality for ‘selfish’ or malicious purposes and contextualises its diverse expressions and dimensions. It specifically offers a systematic taxonomy of eight distinctive deceitful behaviours namely: suppression/camouflage, overloading, mistake, substitution, target conditioning, repackaging capability signatures, amplification and reinforcing impression. These as exemplified by their most common attack forms (i.e., starvation, denial-of-service, session hijacking, man-in-the-middle, poisoning, masquerading, flooding and spoofing) are then benchmarked against five key dimensions referring to time frame (short to long duration), engagement (localised to systemic), urban traffic controller infrastructure (single to multiple components), scale (low to high), and impact (low to high). We then suggest mitigation strategies to protect CAV technology against these dangers. These span from purely technological measures referring to the machine-centric triad of vehicles, communication, and control system including adversarial training, heuristic decision algorithms and weighted voting mechanisms to human factor measures that focus on education, training, awareness enhancement, licensing and legislation initiatives that will enable users and controllers to prevent, control or report deceitful activities.

TRAFFIC MANAGEMENT AND ENGINEERING WORKS FOR HALIM LRT-HSR INTEGRATION STATION IN JAKARTA-CIKAMPEK TOLL ROAD SEGMENT Km.1+280 TO 1+420

The study of traffic engineering management is very different, causing the condition to exist. Traffic Engineering Management (MRLL) can make macroscopic in the pre-feasibility study stage, with concern to guide its macro transportation system, may also include its own details (microscopic). Traffic impact control policies are also included, be it road management efforts, priority management and demand management. As the main project, the project is related to the public road which will giving influence is the interference of over-the-counter so the contractor or the owner of the work must make a real contribution in the distribution of transactions through all that happens. This study was designed using literature and field research conducted and analyzed measurably. In this research, the introduction of the study area is a very important part of preparation in order to better understand the characteristics of the location. The expected results in this study are to analyze the impact that occurs in each condition of the work carried out and provide recommendations for handling the impacts obtained in each of these conditions.

What if Air Quality Dictates Road Pricing? Simulation of an Air Pollution-based Road Charging Scheme

Road tolls serve various purposes, such as to refinance the road infrastructure or to regulate traffic. They are typically levied for the use of a freeway or the entire road network of a region or country. Toll charges may depend on the duration of use, the vehicle's emission class, the time of the day, the distance travelled, or the traffic volume. The primary objective of a few recent toll system deployments is to internalize externalities with respect to vehicle-caused air pollution. However, the air quality along the toll roads has so far not been considered to directly influence road usage prices. This article investigated by simulation the expected monetary expenditure for drivers and the traffic impact of applying a new distance and air pollution-based charging scheme in the metropolitan region of Berlin. The road usage charges were determined on a per-trip basis by taking the vehicle's emission class, the distance travelled, and the air pollution levels along the route into consideration. The simulation results indicate that it is beneficial for drivers to avoid areas of high air pollution in order to reduce the trip's total road usage charges. The average additional detour distance is thereby short in comparison to the route's length and the resulting additional emissions do not increase to the same extent as the number of detours, since detours are partly even shorter in terms of distance. The explorative analysis gives initial insights into the traffic effects of a charging scheme in which air pollution dictates road pricing.

An optimization model for planning road networks that considers traffic noise impact

To reduce potential noise emissions, an optimization model for planning a road network that considers traffic noise impact is proposed. First, the urban traffic noise prediction method is constructed with the road structure as a constraint. Then, the travel time function is built based on the traffic flow model. Furthermore, the road network optimization model is presented based on the minimum network noise cost under different traffic flow states. The results of road structure optimization show the following: 1) Traffic noise cost increases but travel delay increases as travel coefficients increase; 2) The road traffic noise cost of the smaller design speed is lower; 3) The optimal road structures with various travel coefficients are recommended for distinct optimal objects. 4) The difference between the upper and lower limits of traffic noise pollution under different road structures is within 9 dB. This research can provide a reference for noise control.

Position Uncertainty-Tolerant Cooperative Merging Application for Mixed Multilane Traffic

To date, numerous cooperative merging techniques have been developed, with multiple studies demonstrating their potential to improve traffic safety, efficiency, and environmental impact at merging points. However, many of these techniques make assumptions that will almost certainly be questionable in the real world, namely: 1) relative positioning between vehicles will be sufficiently accurate at all times; 2) the merging application-equipped vehicles will not change lanes before reaching the merge point, nor will other vehicles change lanes into their lane; and 3) vehicles with merging-assist technology will not mix with vehicles without the technology. This paper proposes a cooperative merging application which is capable of functioning even when the vehicle's positioning accuracy is severely degraded and all vehicles are free to change lanes into and out of the merging traffic streams. Furthermore, our strategy demonstrates safety, efficiency, and environmental benefits for the overall traffic stream even with technology penetration rates as low as 20%.

Why Is the Grass the Best Surface to Prevent Lameness? Integrative Analysis of Functional Ranges as a Key for Dairy Cows' Welfare.

Simple SummaryLameness is a highly prevalent clinical condition that causes movement disorders in dairy cows worldwide. With an estimated global population of one billion dairy cows, producing 522 million metric tons of milk per year, this problem affects food availability as well as the global economy. While grass is considered to be the best support surface for cattle, in many places it cannot be used, particularly when climate conditions are too harsh for grass to grow or be maintained. In this paper, we investigate whether grass is the best surface to prevent lameness. The answer to this question is fundamental to establishing better farming practices for cattle welfare. We built an integrative analysis of functional ranges to establish the minimum and maximum movement capacities that a cow has, according to the surfaces to which it is subjected in free housing systems. Using this analysis, we identified many aspects that make a grass surface the healthiest option for cattle. However, when grass is not available, this type of strategy can help to find the best characteristics for other possible surfaces. Our study applies movement analysis to one of the most critical problems in the world of livestock management and contributes towards finding the balance between animal welfare and production.Lameness is a painful clinical condition of the bovine locomotor system that results in alterations of movement. Together with mastitis and infertility, lameness is the main welfare, health, and production problem found in intensive dairy farms worldwide. The clinical assessment of lameness results in an imprecise diagnosis and delayed intervention. Hence, the current approach to the problem is palliative rather than preventive. The five main surfaces used in free housing systems in dairy farms are two natural (grass and sand) and three artificial (rubber, asphalt, and concrete). Each surface presents a different risk potential for lameness, with grass carrying the lowest threat. The aim of the present study is to evaluate the flooring type influences on cows’ movement capabilities, using all the available information relating to kinematics, kinetics, behavior, and posture in free-housed dairy cows. Inspired by a refurbished movement ecology concept, we conducted a literature review, taking into account kinematics, kinetics, behavior, and posture parameters by reference to the main surfaces used in free housing systems for dairy cows. We built an integrative analysis of functional ranges (IAFuR), which provides a combined welfare status diagram for the optimal (i.e., within the upper and lower limit) functional ranges for movement (i.e., posture, kinematics, and kinetics), navigation (i.e., behavior), and recovery capacities (i.e., metabolic cost). Our analysis confirms grass’ outstanding clinical performance, as well as for all of the movement parameters measured. Grass boosts pedal joint homeostasis; provides reliable, safe, and costless locomotion; promotes longer resting times. Sand is the best natural alternative surface, but it presents an elevated metabolic cost. Rubber is an acceptable artificial alternative surface, but it is important to consider the mechanical and design properties. Asphalt and concrete surfaces are the most harmful because of the high traffic abrasiveness and loading impact. Furthermore, IAFuR can be used to consider other qualitative and quantitative parameters and to provide recommendations on material properties and the design of any surface, so as to move towards a more grass-like feel. We also suggest the implementation of a decision-making pathway to facilitate the interpretation of movement data in a more comprehensive way, in order to promote consistent, adaptable, timely, and adequate management decisions.

Spatial Distribution Characteristics of VOCs and Its Impact on Ozone Formation Potential in Rizhao City in Summer

GC-SAW was used to carry out online sampling analysis of the main business sources, residential sources, and roads in Rizhao City from August 22 to 29 in 2020. The spatial distribution characteristics of various volatile organic compounds (VOCs) in the atmosphere were obtained, and the chemical reactivity of the main components was studied. The results showed that the VOCs with carbon atoms greater than 5 (VOCC>5) were mainly toluene propylbenzene and n-octane, and the spatial distribution was significant; the average ρ(TVOCC>5) in the port area, downtown area, and industrial area were 80.5, 115.3, and 118.1 μg·m-3, respectively. Combined with road traffic impact and industrial production emissions, the maximum ρ(TVOCC>5) on the main roads in Rizhao City appeared near the Yingbin Road; the concentration value was 164.37 μg·m-3; the ρ(BTEX) in adhesive processing, painting, and glass factories reached 432.34, 1010.84, and 1989.85 μg·m-3, respectively. The chemical reactivity analysis of the main components of VOCC>5 showed that BTEX and n-octane were the important active components of ozone formation in Rizhao City.

Traffic Impact Assessment, Diversion and Management Plan for Surat Metro Line 1, Package –CS3, Surat

Abstract: Package CS3 of Metro Line 1 in Surat City includes 3 stations i.e. Surat railway station, Maskati Hospital and Chowk Bazar. As per proposed Metro plan of Surat city, this line will originate from Sarthana station and will terminate at Dream City. The length of Line 1 is 21.61KM of which 14.59km is elevated whereas 7.02km is Underground and consists of 20 Stations. This metro line envisages use of public transport system in Surat city and shall cater the present and future travel demand of the catchment area and shall also reduce load from road based transport system of the corridor. During the construction phase of any Mass Rapid Transit System (MRTS) running along the Right of Way (ROW) of existing roadway system, Traffic diversion and management plan implementation becomes absolute mandatory to reduce congestion, conflicts increase level of safety and ease construction process. Similarly, for package CS3 of Surat Metro line 1, there is need of preparing an implementing Traffic Diversion and Management plan to create a synergy amongst construction activities, traffic flow, safety of pedestrian and construction worker with minimal impact on surrounding catchment. This study shall provide Traffic diversion and management plan which may help to cater the existing traffic and stir them in a smooth and non-congested flow with the help of signage’s, road markings, etc.

High-Resolution Flood Simulation in Urban Areas Through the Application of Remote Sensing and Crowdsourcing Technologies

High-resolution flood simulation considering the influence of high buildings and fundamental facilities is important for flood risk assessment in urban areas. However, it is also a challenging task due to the difficulties in acquiring detailed topography and monitoring data for model construction and validation. In this study, a high-resolution flood simulation with a grid size of 0.5 m is realized through the use of detailed topography obtained by an unmanned aerial vehicle and real-time flood information acquired from social media. To discover the influence of terrain resolution on flood simulations, the high-resolution simulation results are compared with those with coarser grid resolutions (5, 10, and 20 m) for a flash flood event in Taiwan. In the case with higher grid resolution, the simulation results are in better agreement with the photos from social media in terms of flood extent, depth, and occurrence time. The flood simulation with coarse resolution (>5 m) tends to overestimate the flood duration on roads and provide bias information to decision-makers in the assessment of traffic impact and economic loss.

Traffic Noise Impact Prediction Based on Road Service Level

In this study, noise measurement was carried out on a section of Third Ring Road of Chengdu in China and the environmental parameters around the measuring point were collected besides the actual measurement. Coupled with the satellite map, the section was modeled and the noise impact prediction was employed using Cadna/A software, and the noise impact from 5:00 to 6:00 p.m. of a working day of in a week was evaluated based on the prediction of road service level. Results showed that evaluating only the overall level of the road service should be enough for predicting the noise impact based on the road service level. When using road service level to predict road noise, the relationship between road speed and traffic volume should be verified. If there is a conflict between reality and theory, the actual data should prevail in the prediction. Additionally, relevant road parameters should be collected when determining the road service level. The longer the duration of collecting evaluation parameters, the more accurate the determination of service level and the more accurate and applicable the prediction results.

Medication Adherence and Influencing Factors Among Patients With Severe Mental Disorders in Low-Income Families During COVID-19 Outbreak.

BackgroundThe COVID-19 has spread across the globe in a short time and affected people's life, especially patients with severe mental disorders. Poor adherence to antipsychotics was usually associated with an increasing risk of relapse. This study investigated medication adherence status among patients with severe mental disorders in low-income families during COVID-19 outbreak and the influencing factors.MethodsTo select patients with severe mental disorders in low-income families in central China's Henan Province, we used multi-stage stratified random sampling method. Trained interviewers and psychiatrists collected questionnaire responses from the patients through face-to-face interviews or video interviews. Logistic regression models were used to examine factors that influence the status of medication adherence.ResultsA total of 24,763 valid questionnaires were collected between March 10, 2020, and March 31, 2020. The regular medication rate of patients with severe mental disorders in low-income families during the COVID-19 outbreak was 51.46%. Twelve factors were found to influence medication adherence of investigated individuals. Positive factors for regular medication were younger age, higher education level of patients and their guardians, higher medical expenditure, higher level of self-care ability, having subsidies for care and supervision, having disability certificate and personal care, etc.ConclusionsThe COVID-19 outbreak affected the medication adherence among patients with severe mental disorders in low-income families. The influencing factors are complicated and diverse, including psychological effects, traffic impact, and economy, etc. The government should pay more efforts on social assistance programs and flexibly deal with difficulties during public health emergencies like the COVID-19.

Evaluation of Traffic Impact Assessment at a Large-scale Retail Store Facing Community Road Based on Traffic Surveys After Opening the Store

Evaluation of Traffic Impact Assessment at a Large-scale Retail Store Facing Community Road Based on Traffic Surveys After Opening the Store

A Novel Real-Time Traffic Signal Control Strategy for Emergency Vehicles

Intelligent traffic signal control strategies for emergency vehicles (EV) is a research hotspot in intelligent transportation systems. Its research results will promote the shortening of the travel time of EVs, which is vital to saving lives and reducing property losses. This paper proposes a novel real-time traffic signal control strategy. The first step is called on-demand signal timing for reducing road saturation, which makes it possible that ordinary vehicles(OV) give way to EVs and make EVs travel faster, according to three indicators: the emergency response level(ERL), the congestion level of the road section(CLRS), and the time urgency level(TUL). The second step is called novel signal preemption, which combines non-intrusive preemption and intrusive preemption. This step provides green indicates and makes EVs pass through intersections quickly without stopping. The final step is called the recovery cycle strategy, which restores the road network to the normal situation as soon as possible by using linear programming(LP) to find the shortest green time in each phase after an EV passes the intersection. This paper conducts simulation experiments on an urban traffic simulator SUMO. The experimental results show that our novel strategy can shorten the travel time and reduce the traffic impact on the road network caused by prioritizing the EVs significantly. Compared with the fixed-time control method (FTCM), Min’s flexible signal preemption method(FSPM), and Qin’s intrusive signal preemption method (ISPM), our method can optimize travel time by up to 62.85%, 50.83%, and 11.62%, respectively.

A Study of Traffic Demand Forecasting and Traffic Impact Assessment for location of large facilities

大型施設の立地を検討する場合には、施設が呼び込む交通需要による影響分析を行い、周辺道路や施設への影響が軽微であることを示す必要がある。施設立地により発生する交通需要は、様々な要因によって大きくその性質・規模等が異なるため、検討施設の特性を考慮した影響評価手法を選定する必要がある。そこで本稿では、論文の前半で、交通影響評価に必要となる調査・分析を実施する際に検討すべきポイントや留意点を分析の基本的な流れに沿って示す。後半では、交通影響評価手法の一つである「交差点解析」に焦点を絞り、解析精度に大きく影響を及ぼす「飽和交通流率」の算出方法を事例とともに複数紹介し、各手法のメリット・デメリットを整理したうえで、検討内容や検討段階、業務全体の方向性を見据えた適切な計算手法を選定することの重要性を示した。

ОЦЕНКА ЖИЗНЕННОГО СОСТОЯНИЯ ЗЕЛЕНЫХ НАСАЖДЕНИЙ МАГИСТРАЛЬНЫХ УЛИЦ Г. БИРОБИДЖАНА В РЕЗУЛЬТАТЕ ВОЗДЕЙСТВИЯ АВТОМОБИЛЬНОГО ТРАНСПОРТА

The authors present the results of green infrastructure vital status assessing under the road traffic impact in Birobidzhan. They calculated the index and total one-time emission of carbon monoxide from vehicles in different parts of the town. Most trees near the roads were found to be in impaired state (vital status index 0.50–0.70), especially the coniferous species. The more a distance from the town roads, the larger number of healthy trees was identified. The crossroads, located at the bypass roads with heavy trucks traffic, have the highest values of CO emissions (from 0.035 to 0.045 g/s). It was found a strong correlation between CO emissions and the vital status index of roadside green infrastructure, demonstrated by the r-Pearson correlation coefficients critical values (p=0.2319).

Brazilian truckers’ strike and particulate matter (PM10) concentration: Temporal trend and time series models

High particulate matter (PM) emissions from vehicular traffic impact air quality in urban areas. In 2018, a truckers’ strike interrupted some of the services in Brazil, leading to a fuel outage in several cities that significantly reduced the flow of vehicles. This study evaluated air quality during the strike in two cities (Limeira and Campinas) in Southeastern Brazil. PM10 concentration was analyzed in the periods before (BTS — 05/01/2018 to 05/22/2018), during (DTS — 05/23/2018 to 05/30/2018), and after (ATS — 05/31/2018 to 06/30/2018) the strike using the Theil-Sen method and the Autoregressive Integrated Moving Average model with Exogenous Variables (ARIMAX). A reduction in the PM daily mean concentration in both cities occurred during the strike. Considering the daily peak time of vehicular flow (6:00 p.m.), the PM10 concentration was 20% higher in the BTS period compared to the DTS period for both cities. In comparison, the ATS period showed concentrations 17% (Limeira) and 7% (Campinas) higher when compared with the DTS period. The variations were statistically significant based on the time series models, and the influences of wind speed, rainfall on the sampling day and the day before sampling, and weekends were also evaluated. It was also possible to verify the contribution of the truckers’ strike to the PM10concentration in the two cities evaluated. In Limeira, truck traffic had a greater influence on the concentration of PM10, while in Campinas, the contribution of trucks was like that of light vehicles. Based on the variation of the PM10 concentration, the influence of changes in vehicle emission dynamics, one of the main sources of emission in the regions studied, was observed. The results indicate that restricting vehicular traffic had an immediate impact on improving air quality. Therefore, public investment in other types of transport and traffic control policies are suggested.

Experimental Observation and Analysis of Traffic Impact on Tibetan Antelopes on the Qinghai‐Tibet Highway

Highways that cross natural reserves are an intrusion with a nonnegligible negative impact on the behavior of wild animals and have numerous and diverse ecological impacts on wildlife near road areas. Field experiments were carried out to collect traffic flow data on the Qinghai‐Tibet Highway on the Qinghai‐Tibet Plateau, China, and the behavior of the Tibetan antelope crossing the highway was observed. The relationships between the percentage of antelopes successfully crossing the highway and the different traffic flows were analyzed. The results demonstrate that the traffic volume is the main factor affecting the success rate of Tibetan antelopes when crossing the highway, displaying a nonlinear negative correlation. Furthermore, the behavioral responses of the Tibetan antelopes within 500 m of the Qinghai‐Tibet Highway before and after different parking behaviors were observed and the proportions of the different behaviors exhibited by the Tibetan antelopes affected by different driver parking behaviors were analyzed. Parking behaviors were found to have the most significant effect on Tibetan antelope behavior within 400 m of the highway, where parking with somebody getting out having the most prominent impact. The results of this study can guide engineering measures to protect wildlife in the plateau region.

The Traffic Impact Analysis on Province Roads as Requirement for Licensing Services Provision in Building Sector

The purpose of this research is to study and analyze the impact of traffic on provincial roads as a permit requirement in the building sector by local governments based in Central Java and the solution efforts. The approach method used in this research is sociological juridical. The results of the study indicate that: the implementation of the traffic impact analysis on provincial roads encountered obstacles, among others, before the implementation of the traffic impact analysis, difficulties were the preparation of the required documents for the implementation of the traffic impact analysis, and in the implementation of the traffic impact analysis, including those related to supervision issues, not guided by the authority to analyze traffic impacts based on road status, do not understand the laws and regulations and related provisions, and lack of law enforcement.

Multi-task equilibrium scheduling of Internet of Things: A rough set genetic algorithm

The Internet of Things (IoT) server suffers from numerous business traffic with network bandwidth growth, resulting in downtime. Providing business support without affecting the user experience is the primary problem that IoT companies need to consider and solve in the face of traffic impact. This paper proposes a load balancing scheduling algorithm based on Particle Swarm Optimization Genetic Algorithm (PSO-GA) for IoT clusters. The algorithm uses CPU occupancy rate, memory occupancy rate, network bandwidth occupancy rate, and disk Input and Output (IO) occupancy rate to comprehensively measure the server node load and establish a resource balance model. The fitness function is used to quantify the influence as the basis of weight adjustment. Then, the Particle Swarm Optimization (PSO) algorithm uses the disturbance factor and contraction operator. The optimized algorithm is used to calculate the optimal solution of the fitness function and obtain the optimal weight. Finally, the PSO-GA algorithm is simulated, tested, and compared with the other three load balancing algorithms. As seen from the test results of response delay, throughput, request error rate, and resource utilization, the performance of this algorithm is improved by more than 5% compared with the performance of the traditional method, and the optimization ability is improved obviously. The research content of this paper provides a new way to alleviate the network load, reduce the server overload, congestion, downtime, and other problems, and realize the multi-task balanced scheduling of IoT.