Traffic Volume Variation Research Articles

Traffic count data analysis using mixtures of Kato–Jones distributions

Abstract We discuss the modelling of traffic count data that show the variation of traffic volume within a day. For the modelling, we apply mixtures of Kato–Jones distributions in which each component is unimodal and affords a wide range of skewness and kurtosis. We consider two methods for parameter estimation, namely, a modified method of moments and the maximum-likelihood method. These methods were seen to be useful for fitting the proposed mixtures to our data. As a result, the variation in traffic volume was classified into the morning and evening traffic whose distributions have different shapes, particularly different degrees of skewness and kurtosis.

Visual Analytic for Traffic Impact Assessment

This study strives to promote the state of traffic impact assessment through high-end visual analytics by incorporating spatial and temporal data visualization to enhance traffic management. Based on a dataset on traffic flow at three major intersections, we married data cleaning, integration, and transformation to set out for a detailed visual analysis. Thus, the critical materials comprise the traffic count in multiple lanes, vehicle types, and saturation flow rates to understand the road network's capacity. They essentially explored the traffic volume variations daily and hourly and pattern identification using heat maps, parallel coordinate charts, and bar plots. Thus, the findings expose the remarkable traffic volume and pattern differences by distinguishing peak and off-peak hours on weekdays and weekends. The level of service at each junction was determined by the volume-to-capacity ratio, identifying potential congested areas. As such, this work points to the importance of further improvements to visual analytic techniques to accurately predict traffic patterns and evaluate traffic management strategies effectively. Predictive models based on visual analytic findings can pave the way for proactive traffic control and congestion mitigation, making urban traffic management more efficient and safer. The current study provides a scaffold for additional exploration of the above-detailed methods and their penal outcomes in urban development planning and policy provision in terms of developing sustainable traffic control strategies and real-time decision-making improvements.

Influence of Land use and Traffic on Road Safety Along Jimeta Bypass

Many roads in some urban areas in developing countries, such as Nigeria, are characterized by a high level of traffic and, because of limited resources, receive inadequate forms of protection and inappropriate maintenance strategies. Some of the roads have become part of a landfill dump embankment, which cause defects that contribute to road accidents. The study sought to investigate the effect of land use and traffic on road safety along the Jimeta Bypass Road. Vehicle enumeration was carried out by manual counting. Road traffic accident data for the analysis was obtained from the Federal Road Safety Commission in Yola. Physical inspection of the pavement section revealed that it was in an unsuitable condition for riding and safety. The findings also revealed that the road pavement had an annual average daily traffic volume of about 1470 vehicles per day, with tricycles having the highest traffic volume and luxurious buses having the lowest traffic volume. Analysis of variance showed that there was no significant variation in traffic volume between periods (morning, afternoon, and evening); traffic only differs between vehicle types. Accident data collected was found to highly correlate with traffic volume, an indicator that the high traffic volume along the Jimeta Bypass Road could be responsible for the occurrence of road traffic crashes along the road. The study findings also indicate there is need to adopt the maintenance culture by Road Authorities and relevant stakeholders so as to address the failure condition at the onset.

Enhancing urban real-time PM2.5 monitoring in street canyons by machine learning and computer vision technology

During peak hours, both pedestrians and drivers face extended exposure to road air pollution, raising the risk of respiratory diseases. Variations in traffic volume, building attributes, and weather conditions in street canyons lead to differing exposure levels across road sections and intersections. Though deploying numerous sensors provides real-time access to PM2.5 levels in each road section, they cannot interpret the impact of weather, traffic, and buildings on these levels. Therefore, in Lanzhou City, we attempted to use Computer Vision Technology (CVT) to extract real-time traffic volume and street-view features from traffic images for PM2.5 concentration prediction, and to interpret the impact of road environment changes on PM2.5 levels. Results show that by using the Extreme Gradient Boosting (XGB) regression model, the coefficient of determination for PM2.5 prediction reaches R2=0.956. Meteorological conditions, traffic volume, and buildings are key variables in predicting road PM2.5 concentrations. Meteorological conditions control the continuous fluctuation of road PM2.5 levels, while traffic volume can lead to sudden changes in PM2.5 levels. The research indicates that the combination of traffic cameras and CVT can acquire road PM2.5 concentrations, contributing significantly to rapidly understanding road pollution status, identifying highly polluted roads, and conducting exposure assessments of roadways.

Investigation on permanent deformation in steel-concrete composite beam bridge deck pavement under temperature-load coupling effect

Rutting is one of the common distresses observed in asphalt pavement, influenced by temperature and load conditions. To clarify the permanent deformation behavior of steel-concrete composite beam (SCCB) bridge deck pavement under temperature-load coupling effect and provide references for the distress cause analysis, five typical SCCB bridge deck pavements were selected. The temperature distribution and the temperature stress of the pavement structures were analyzed by numerical simulation under periodic temperature variations. In addition, considering the daily variation in traffic volume, the permanent deformation of the five pavement structures were calculated under temperature-load coupling effect. Finally, the influence of heavy load on the development of rutting distress was also investigated. The results show that the temperature field and temperature stresses within the SCCB bridge deck pavement exhibit periodic variations under periodic temperature variations. Additionally, after 500,000 times of standard axle load application, “EA + SMA” exhibits the smallest permanent deformation and the best resistance to rutting distress under temperature-load coupling effect. Finally, heavy load conditions have a great influence on the permanent deformation of SCCB bridge deck pavement. In areas with severe rutting distresses, it is recommended to use “EA + SMA” pavement structure in SCCB bridge.

Eliminating the impacts of traffic volume variation on before and after studies: a causal inference approach

A before and after study framework measures the outcomes in a group of participants before introducing an intervention, and then again afterward. In this study, a before and after study framework is adopted to evaluate the effectiveness of transportation policies and emerging technologies. Generally, the outcome of every before and after study will help decision-makers to monitor and understand the effects of interventions and to make sound decisions. However, many factors such as seasonal factors, holidays, and lane closures might interfere with the evaluation process by inducing variation in traffic volume during the before and after periods. In practice, limited effort has been made to eliminate the effects of these factors. In this study, an extreme gradient boosting (XGBoost)-based propensity score matching (PSM) method is proposed to reduce the biases caused by traffic volume variation during the before and after periods. In order to evaluate the effectiveness of the proposed method, a corridor in the City of Chandler, Arizona where an advanced traffic signal control system has been recently implemented was selected. The results indicated that the proposed method can effectively eliminate the variation in traffic volume caused by the COVID-19 during the evaluation process. In addition, the results of the t-test and Kolmogorov-Smirnov (KS) test demonstrated that the proposed method outperforms other state-of-the-art PSM methods. The application of the proposed method is also transferrable to other before and after evaluation studies and can significantly assist transportation engineers to eliminate the impacts of traffic volume variation on the evaluation process.

Significant differences in black and brown carbon concentrations at urban and suburban sites

Light-absorbing carbonaceous particles (LAC) may cause and/or exacerbate non-communicable diseases, interfere with the Earth's radiative balance, darken urban buildings and impair vistas. In this study, we explored the temporal behaviour of LAC concentrations measured at wavelengths of 370 nm (brown carbon, BrC) and 880 nm (black carbon, BC) at two sites of a mid-sized city in Brazil. We observed sharp changes in LAC concentrations at the city centre site in response to variations in traffic volume. The highest concentrations were observed when winds originated from both the city core and from the direction of the bus terminal. The suburban site exhibited a notably uniform diurnal pattern and consistently lower LAC concentrations throughout the day. Nevertheless, substantial increases during the evening led to mean BrC and BC concentrations (2.6 and 2.2 μg m−3, respectively) comparable to daytime peaks observed in the city centre (3 μg m−3 and 2.5 μg m−3). This phenomenon was attributed to the burning of residential waste and overgrown vegetation in nearby vacant lots. Moreover, the highest concentrations coincided with periods of low wind speeds, usually linked to non-buoyant plumes from point sources. BrC concentrations surpassed BC concentrations, even at the city centre site. Not only was the Ångström absorption exponent (Å370/880) larger at the suburban site compared to the city centre (95th percentiles of 1.73 and 1.38, respectively), but it also exhibited a wider span. Overall, the combined LAC and Å370/880 data indicated that i) biomass burning is a major source of LAC at the suburban site; ii) at the city centre, bare BC particles may become internally mixed with BrC from biomass or fossil fuel emissions and enhance absorption at lower wavelengths. The occurrence of LAC peaks outside the evening rush hours suggests that other sources but on-road vehicular emissions may contribute to the deterioration of the air quality in the urban core. Tackling air quality across the urban perimeter requires targeting other potential sources but traffic emissions.

Assessing the impact of heavy rainfall on the Newcastle upon Tyne transport network using a geospatial data infrastructure

Extreme weather conditions can adversely impact transport networks and driver behaviour, leading to variations in traffic volumes and travel times and increased accident rates. Emergency services that need to navigate to an accident site in the shortest possible time require real-time location-based weather and traffic information to coordinate their response.We therefore require historical and high-resolution temporal real-time data to identify districts and roads that are prone to different types of incidents during inclement weather and to better support emergency services in their decision-making. However, real-time assessment of the current transport network requires a dense sensor network that can provide high-resolution data using internet-enabled technology.In this research, we demonstrate how we obtain historical time-series and real-time data from sensors operated by the Tyne and Wear Urban Traffic and Management Control Centre and the Urban Observatory based at Newcastle upon Tyne, UK. In the study, we assess the impact of rainfall on traffic volume and travel time, and the cascading impacts during a storm event in Newcastle during early October 2021. We also estimate the economic cost of the storm, with regards to transport disruption, as the cost of travel, using the “value of time” based on Department for Transport guidelines (2021).Using spatial-temporal analysis, we chose three locations to demonstrate how traffic parameters varied at different times throughout the storm. We identified increases in travel times of up to 600% and decreases in traffic volume of up to 100% when compared to historical data. Further, we assessed cascading impacts at important traffic locations and their broader implications for city areas. We estimated that the storm's economic impact on one sensor location increased by up to 370% of the reference value.By analysing historical and real-time data, we detected and explained patterns in the data that would have remained uncovered if they had been examined individually. The combination of different data sources, such as traffic and weather, helps explain temporal fluctuations at locations where incidents were recorded near traffic detectors.We anticipate our study to be a starting point for stakeholders involved in incident response to identify bottleneck locations in the network to help prepare for similar future events.

Key Event Analysis of Customer Behaviour Using Clickstream Data in Airline Market

COVID-19 travel restrictions have significantly affected airline markets. A behavioural analysis is presented of 1 million customers of a major airline company over 3 years, using clickstream data, combined with COVID-19 Government Response Tracker (OxCGRT) data. The main results are that pre-pandemic, the variations in traffic volumes were seasonal and there was a consistent ratio of search (76%), buy (5%) and e-service (19%) activities across the year. The start of the pandemic caused a sharp drop in website traffic, which minimised at 1/3 of normal levels and then gradually increased so that post-covid volumes are now 20% higher, which reflects pent-up demand and consumer confidence. At the start of the pandemic, e-service increased sharply and then dropped to below pre-pandemic levels. The proportion of buyers decreased significantly at the start of the pandemic and then returned to just below pre-pandemic levels. The proportion of searchers remained high throughout the pandemic. Comparing post-pandemic with pre-pandemic, the proportion of searchers remains higher, buyers has dropped, and e-service remains the same. These results show the importance of distinguishing between different types of online activity, defined by three broad phases in the customer lifecycle: search, buy and e-service, which can only be achieved by using raw, clickstream data. Typically, web server analytics software employ aggregate data and it is much more difficult to uncover nuanced changes that are apparent at the granular level of individual clickstream data. The contribution of the research is to demonstrate a novel analytical methodology that exploits detailed clickstream data to characterize the dynamic changes in customer behaviour, which gives important insights into how online markets change in response to exogenous shocks by enabling key event analysis. Using the approaches outlined in this paper, airlines and e-commerce companies can anticipate, measure behavioural changes and be pro-active in their strategic responses to significant external events in the competitive environment that are likely to affect customer behaviour.

Nash Equilibrium and Stackelberg Approach for Traffic Flow Optimization in Road Transportation Networks—A Case Study of Warsaw

The article discusses the issue of modelling traffic flows and the transport network. Faced with an increase in the number of vehicles in road networks, the problem of congestion and the need to optimise traffic and adapt the transport infrastructure to changing demand are growing, especially in large cities. With this in mind, the authors of this publication developed a model of the road network in the north-eastern part of the Warsaw agglomeration based on the proposed algorithm. Two methods were used to optimise the distribution of traffic flows: the Nash equilibrium and the Stackelberg approach. The Nash equilibrium assumes the aim of achieving equal average times on all roads for each origin–destination (O-D) pair. This describes the state pursued by a decentralised system guided by the individual benefits of the traffic users. On the contrary, the Stackelberg approach aims to achieve optimal travel times for the entire system. The study was carried out for three scenarios that differed in the assumed traffic demand on the road network. The basic scenario assumed the average hourly traffic demand during the morning peak hour based on traffic measurements. On the other hand, the two alternative scenarios were developed as a 10% variation in traffic volumes from the baseline scenario. On the basis of the results, it was concluded that an increase in traffic volumes for all O-D pairs could result in a decrease in traffic volumes on some links of the road network. This means that the transport network is a complex system and any change in parameters can cause significant and difficult to predict changes. Therefore, the proposed approach is useful in terms of traffic forecasting for road networks under conditions of changing traffic flow volumes. Additionally, the total travel time for the entire system differed for each scenario by a percentage difference of 0.67–1.07% between the optimal solution according to the Nash equilibrium and the Stackelberg approach.

Dynamic Multi-View Graph Neural Networks for Citywide Traffic Inference

Accurate citywide traffic inference is critical for improving intelligent transportation systems with smart city applications. However, this task is very challenging given the limited training data, due to the high cost of sensor installment and maintenance across the entire urban space. A more practical scenario to study the citywide traffic inference is effectively modeling the spatial and temporal traffic patterns with limited historical traffic observations. In this work, we propose a dynamic multi-view graph neural network for citywide traffic inference with the method CTVI+. Specifically, for the temporal dimension, we propose a temporal self-attention mechanism that is capable of learning the dynamics of traffic data with the time-evolving traffic volume variations. For spatial dimension, we build a multi-view graph neural network, employing the road-wise message passing scheme to capture the region dependencies. With the designed spatial-temporal learning paradigms, we enable our traffic inference model to encode the dynamism from both spatial and temporal traffic patterns, which is reflective of intra- and inter-road traffic correlations. In our evaluation, CTVI+ achieves consistent better performance compared with different baselines on real-world traffic volume datasets. Further ablation study validates the effectiveness of key components in CTVI+. We release the model implementation at https://github.com/dsj96/TKDD.

Estimation of travel time variation caused by transport infrastructure development

Purpose The expected benefits of newly developed transportation infrastructures are the saving of travel time and further promoted transport economics. There is a need for a methodology of travel time estimation with acceptable robustness and practicability. Macroscopic fundamental diagram (MFD) represents the overall traffic performance at a network level by linking average flow, speed and density. MFD can be used to estimate network state and to describe various traffic management strategies. This study aims to describe the effect of new infrastructure development on the network performance using the MFD framework. Design/methodology/approach The scenarios of Islamabad Road network before and after the infrastructure construction were simulated, in which the floating car data set (FCD) for multiple modes was extracted. MFD has been formed for the whole region and partitioned region, which was divided on the basis of infrastructural changes. Moreover, this study has been extended to calculate travel time for multiple modes using the MFD results and the Bureau of Public Roads (BPR) function at a neighborhood level. Findings MFD results for the whole network showed that the speed of traffic improves after the construction of new infrastructure. The travel time estimates using MFD results were dependent on the speed estimates, whereas the estimates obtained using the BPR function were found to be dependent on the traffic volume variation during different intervals of the day. By using the FCD for multiple modes, travel time estimates for multiple modes were obtained. The BPR function method was found valid for estimating travel time of traffic stream only. Originality/value This paper innovatively investigates the change in network performance for pre-construction and post-construction scenarios using the MFD framework. In practice, the approach presented can be used by transportation agencies to evaluate the effect of different traffic management strategies and infrastructural changes.

Biplots of kinematic variables and pollutant emissions for an intercity corridor

A thorough understanding of driver behavior is an important step to improve the environmental performance of road traffic. Accurate data analysis tools can be valuable to identify these concerns. The present study explores relationships between driving patterns, tailpipe emissions, and road differentiation by using Principal Component Analysis Biplot. This statistical methodology is suitable to identify patterns hidden in data and can be used as a visualization tool. In this study, the key variables included were: speed, engine speed (RPM), acceleration, and vehicular jerk (first derivative of acceleration), as kinematic variables, and carbon dioxide (CO2), nitrogen oxides (NOx), and VSP (Vehicle Specific Power) mode, as pollutant emission variables. For this purpose, second-by-second vehicle dynamics and tailpipe emissions data were collected in three passenger cars with different powertrains (gasoline, diesel, and hybrid) along with different types of routes (one partly urban/rural and two motorways with variations in traffic volumes) in Aveiro Region (Portugal). Results revealed that Biplots allowed to distinguish different driving behaviors, separate route types (urban/rural from motorways), establish some remarks about emissions, and also present the correlated variables in a single plot. Therefore, this technique can be considered as a useful visualization tool to explore real traffic-related data.

Analysis of the Traffic Parameters on a Section in the City of the National Road during Several Years of Operation

The study carried out an analysis of the vehicle traffic parameters on a national road in 2011-2016. The variability and uncertainty of results were evaluated. An analysis of traffic data recorded on the city's entry and exit lanes was carried out. The variations in traffic volume are of interest e.g. in dynamic traffic management systems and navigation services, examining the benefits of flexible work time and places and assessing the environmental effects of traffic congestion. Research has shown that the assumption that lanes perform equally is not always true. Traffic volume models should be periodically calibrated taking into account the shape of the daily profile, which may, for example, allow public transport timetables to be more responsive to the needs of travelers.

Influence of mainline schedule flexibility and volume variability on railway classification yard performance

Single-railcar shipments of freight transported by multiple freight trains and sorted at several classification (marshalling) yards between origin and destination remain an important source of traffic and revenue for North American railways. To aid railroads in making business decisions regarding train operating plans and efficient use of yard capacity, this research investigates the influence of inbound traffic volume variation and schedule flexibility on classification yard performance. Simulation experiments using YardSYM, a discrete-event simulation model developed for hump classification yard analysis, quantify the interaction of these factors through different yard performance metrics. Simulation results suggest increasing schedule flexibility causes classification yard performance to decline while increasing volume variability has a less pronounced effect. For the simulated yard, the combined effect of train arrival time and volume variability increased average railcar dwell by over 3 h, and reduced railcar connections achieved by ten percent. Bunching train departures increased dwell by nearly 2 h and cut on-time train originations in half. To better understand the implications for network performance, additional analysis of the propagation of schedule flexibility and volume variation through classification yards demonstrates how schedule flexibility is amplified and dampened to medium values related to the consistency of the train assembly process.

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.

Impacts of traffic on roadside particle variations in varied temporal scales

Traffic is a significant pollution source in cities and has caused various health and environmental concerns worldwide. Therefore, the improved understanding of traffic impacts on particle concentrations could help mitigate air pollution. In this study, field measurements were conducted beside an arterial road in Shanghai, China, to obtain the fine-scale temporal variations in traffic volumes, meteorological parameters, and size-resolved particle concentrations. The generalized additive model was developed to reveal their associations under different analysis intervals (time scales). Results indicate that on-road traffic greatly increased the concentration and variability of roadside particles. Variations of roadside fine particles (<1 μm, having high homogeneity) closely followed those at the background point, but background variations of coarse particles (having high heterogeneity) were negligible compared with the roadside point. Roadside particles showed high variability on the 10-s scale and presented frequent stochastic spikes above the background level. The particle variability was greatly reduced on the 60-min scale, exhibiting stable variations. The association between traffic and roadside particle variations was weak on a fine temporal scale (5-min scale). However, the association showed a pronounced increase when the analysis interval was increased (60-min scale). This is because extending the analysis interval could reduce the influence of random factors (sample randomness) and emission lagging effects on the results. Moreover, with the increase of the analysis interval, the improvement of associations between variables can be well characterized by exponential curves, which could help determine the optimal analysis interval. Additionally, heavy-duty vehicles showed a lot more significant influence on particle variations compared with that of light-duty vehicles, and should be regarded as the key governance target for on-road pollution control. These findings broaden the knowledge of traffic impacts on particle concentrations, which could provide implications in air pollution abatement.

Urban Traffic Detectors Data Mining for Determination of Variations in Traffic Volumes

This paper analyses road traffic volumes in the urban environment for the purpose of traffic planning and creation of traffic models. For modelling traffic in a certain area, the initial information about transport demand and distribution in given area is required. The demand for transport is further re-distributed to the transport network and measured against the current road traffic volumes / intensity of traffic. Traffic volumes over time are characterized by various periodic and non-periodic influences (variations). By studying these variations, the tools can be specified for making the final estimate of traffic volumes for a specific time period, a specific type of road or specific vehicle category, and for improving the traffic models for a specific area. In this paper, the authors study time variations in traffic volumes using the data obtained from vehicle detectors for monitoring traffic located on roads in the city of Ceske Budejovice, the Czech Republic.

The built environment and asthma: Los Angeles case study

This study aimed to identify urban characteristics that contribute to the incidence of asthma, applying various elements of built environment relevant to variations in traffic volume and traffic-related air pollution. We used built environment metrics in three dimensions (density, diversity, design) and examined whether those metrics are associated with asthma emergency department (ED) visits among 2343 census tracts in Los Angeles County by applying spatial epidemiologic tools (spatial regression, GIS mapping). We found that increases in population density, land use mix, and the share of open/recreation area are negatively associated with asthma ED visits but found no significant effects of household and street density. The results of neighborhood risk factors (percent of African American, less than high school, median household income, unemployment) confirm the overwhelming impact of the disadvantaged neighborhoods on asthma epidemiology. The findings indicate that high population density contributes to a reduction in acute care utilization for asthma and underscore the beneficial roles of land use mix and a larger share of open/ recreation space for respiratory health by promoting physical activity and improving air quality.

Determination of Passenger Car Unit for Urban Roads: A Case Study in Addis Ababa

In Ethiopia, specifically on Addis Ababa city roads operating, vehicles are mixed with varying static and dynamic traffic characteristics that are very challenging for the traffic analyst to come up with a sole solution to the traffic malady. Under heterogeneous traffic, each vehicle type cannot be compared with other vehicle types since it shows different effects on the road. It could be observed that the majority of the drivers in Addis Ababa road networks do not follow proper lane discipline to use any available space on the road sections. Currently, Ethiopia is using the passenger car unit values from other countries manual, which doesn't represent the local traffic and roadway conditions. This study was initiated to establish the PCU values that will cater the local settings. Hence, this study was focused on estimating the PCU values of multilane highways in Addis Ababa city and determining the influence of traffic volume and width of the road on the values of PCU. The PCUs were estimated for four vehicle types based on the variation of traffic volume and carriageway width. Dynamic PCU method was used to determine PCU values, and a linear regression analysis was performed for model development. The developed model is used to estimate PCU values having the same traffic conditions and roadway conditions found in other countries. The study results indicated that as the traffic volume and carriageway width increases, the passenger car unit value also increases. Also, it was found out that the results of PCU values in this study showed higher values than those provided in Highway Capacity Manual (HCM). Hence, the PCU values obtained are recommended to reflect the existing traffic condition in the locality.