Changing Traffic Conditions Research Articles

The direction of urban development and policy implications for response to climate change – the case of Gwangju Metropolitan City, Republic of Korea

ABSTRACTThis study investigates the case of Gwangju Metropolitan City, which has been developing preemptive policies to cope with the impact of climate change. It is necessary and important to examine empirical data on how Gwangju Metropolitan City has promoted its preemptive response to climate change. The concept of climate change, the status of climate change, and the energy consumption of each sector were first identified, and trends and prospects of changes in traffic conditions, and the status of parks and green spaces were examined using indicators from a 10 year period (2002–2011). Relevant variables were selected from the climate carbon map constituents presented in the ‘2030 Gwangju Metropolitan City Master Plan’ report, and key urban indicators and low-carbon indicators presented in ‘A Study on the Construction of Climate Carbon Map in Gwangju’. Cluster analysis using the factor scores of the factors extracted through these variables, followed by principal component analysis and k­medians nonhierarchical cluster analysis, were carried out using STATA 11.1. Thereafter, SWOT analysis was performed to classify the environmental factors (strengths and weaknesses) and external environmental factors (opportunities and threats) into categories based on the variables extracted from Gwangju Metropolitan City’s response to climate change. The purpose of this study is therefore to classify the above-mentioned trends in urban development responses to climate change into factorization types classified by principal component analysis, and to analyse the results of SWOT analysis and factors that can mitigate the urban heat island phenomenon based on the derived factors and the development of public transit-oriented urban development (TOD). This study serves not only as basic research data in presenting urban development strategies for future climate change, but also suggests policy implications.

Velocity Prediction of Intelligent and Connected Vehicles for a Traffic Light Distance on the Urban Road

Accurate vehicle velocity prediction has important theoretical value and widespread applications in many areas, such as optimal control of vehicle propulsion system, eco-driving, and advanced driver assistance systems. However, for dynamic changes of traffic condition caused by traffic lights, intersections, and other factors, it is hard to predict the vehicle velocity accurately on the urban road. In this paper, we present a novel vehicle velocity prediction algorithm for intelligent and connected vehicles based on the historical driving data of the preceding vehicle and traffic light information. First, the basic driving rules on the urban road are studied in two different driving scenarios. Then, a vehicle trajectory generation algorithm (VTGA) is proposed to generate the vehicles’ trajectories according to the basic driving rules. To identify vehicles’ quantity and the global positioning system information of each vehicle in the unknown area, an identification algorithm (IA) is designed based on the VTGA and genetic algorithm. Finally, a vehicle velocity prediction algorithm is applied to predict the velocity of the target vehicle based on the VTGA and the results of IA. To verify the method proposed in this paper, the next generation simulation database is utilized. The results demonstrate that the accuracy of the vehicle velocity prediction has a significant improvement in the urban network, and the root-mean-square error reduces from 0.50 ~ 4.78 m/s (5 ~ 20 s) to 0.7594 ~ 0.9166 m/s (9.3 ~ 43.8 s), when compared with methods of other studies.

Space-time dynamics: A modeling approach for commuting departure time on linked datasets

Commuters' departure time related decisions are important in time geography. Analytic tools have been proposed to capture the inherent choice determinants both in time and space. Although the dynamic aspects of the problem have been identified, most of the existing studies are based on static models. In this paper, a dynamic modeling framework is proposed to explore the relationship between commuters' departure time choices and the evolution of en route traffic. A data linkage method is developed to create an integrated dataset that enables the observation of commuters' reaction to changes in travel time and traffic conditions over time. A regional household travel survey is linked to travel information obtained from the Google Maps application program interface (API), creating a synthetic longitudinal dataset. Two decision rules are applied to model commuters' response to the evolution of traffic. The results indicate that travel time, distance to work location, flexibility in working schedule, expected arrival time, and commuters' sociodemographic influence departure time choices. It is also found that accounting for dynamics improves model fit and out-of-sample predictions. Both the dynamic model and the proposed data linkage method contribute to the understanding of human activities in space and time and can be used to enhance transportation demand analysis and urban policy studies.

Imitation modeling as a forecast of transport flows self-organization

The problem of reorganizing the road traffic at the time of repair roads is always worthwhile, since the roads are constantly becoming unusable and there is a need to maintain them in proper condition. Frequent repairs are caused by an increase in the number of cars on the roads, as the road surface wears out faster because of this. During repair, the entire repair section often overlaps, and due to the redirection of traffic flows, the load on the adjacent sections changes. This paper analyzes the method of predicting the traffic situation, in which the load on the road network increases due to changing traffic conditions. The application of the simulation method can significantly simplify the task of predicting the behavior of drivers in traffic, as well as help to develop in advance measures to prevent congestion and accidents.

Modeling of time-dependent safety performance using anonymized and aggregated smartphone-based dangerous driving event data

Safety performance functions (SPFs) are generally used to relate exposure to the expected number of crashes aggregated over a long time (e.g. a year) by holding all other risk factors constant, and to identify hotspots that have excessive crashes regardless of different time periods. However, it is highly likely that the relationships of exposure, risk factors and crash occurrence can vary across different times of day. This study aims to establish time-dependent SPFs for urban roads by using large-scale dangerous driving event data captured by smartphones in different times of day. Multivariate conditional autoregressive (MVCAR) models are developed to jointly account for spatial and temporal dependence of crash observations. Results of two-sample Kolmogorov-Smirnov tests affirm the heterogeneity of the safety effects of dangerous driving events in different time periods. Time-dependent hotspots are identified using potential for safety improvement (PSI) metric. The assumption here is that due to the change of traffic conditions and environment across different times of day, safety hotspots for different time periods should be different from each other. According to the results of Wilcoxon signed-rank tests, hotspots identified by times of day are found to be mostly different from each other. The findings of this study provide insights into temporal effects of risk factors and can support the development of time-dependent safety countermeasures. Besides, this study also shows the potential of leveraging anonymized and aggregated dangerous driving data to assess traffic safety issues.

A Unified Spatio-Temporal Model for Short-Term Traffic Flow Prediction

This paper proposes a unified spatio-temporal model for short-term road traffic prediction. The contributions of this paper are as follows. First, we develop a physically intuitive approach to traffic prediction that captures the time-varying spatio-temporal correlation between traffic at different measurement points. The spatio-temporal correlation is affected by the road network topology, time-varying speed, and time-varying trip distribution. Distinctly different from previous black-box approaches to road traffic modeling and prediction, parameters of the proposed approach have physically intuitive meanings which make them readily amendable to suit changing road and traffic conditions. Second, unlike some existing techniques that capture the variation of spatio-temporal correlation by a complete re-design and calibration of the model, the proposed approach uses a unified model that incorporates the physical factors potentially affecting the variation of spatio-temporal correlation into a series of parameters. These parameters are relatively easy to control and adjust when road and traffic conditions change, thereby greatly reducing the computational complexity. Experiments using two sets of real traffic traces demonstrate that the proposed approach has superior accuracy compared with the widely used space–time autoregressive integrated moving average (STARIMA) and the back propagation neural network approaches, and is only marginally inferior to that obtained by constructing multiple STARIMA models for different times of the day, however, with a much reduced computational and implementation complexity.

Spatio-Temporal Analysis for SINR Coverage in Small Cell Networks

While the growth of mobile applications brings enjoyment to daily life, it also imposes more complicated traffic situations on wireless networks. A complete understanding of the impact from the traffic profile is thus essential for network operators to deploy next generation small cell networks. In this paper, based on stochastic geometry and queuing theory, we develop a mathematical framework that captures the interplay between the spatial location of small access points, which determines the magnitude of mutual interference, and their temporal traffic dynamic. We derive a tractable expression for the SINR distribution, and verify its accuracy via simulations. Based on our analysis, we find that: 1) under the same configuration, when traffic condition changes from light to heavy, the corresponding SINR requirement can differ by more than 10 dB for the network to maintain coverage; 2) the SINR coverage probability varies largely with traffic fluctuation in the sub-medium load regime, whereas in a scenario with a very light traffic load, the SINR outage probability increases linearly with the packet arrival rate; and 3) spatial densification can boost the cell-edge rate almost linearly in the dense regime, and thus confirms the appeal for ultra-dense deployment of small cells in the next generation wireless network.

Intelligent design and implementation of blockchain and Internet of things–based traffic system

With the continuous development of Internet of things, all kinds of smart systems are quickly evolving to make our day-to-day life smoother and safer. Like many other sectors, transportation has entered a period of rapid change. Intelligent Traffic System is one of the fastest-growing fields within the smart systems, which is expected to increase road safety, mitigate traffic congestion, and enable fuel efficiency. The main functionalities of Intelligent Traffic System are as follows: (1) monitoring real-time traffic conditions in specific areas, (2) locating traffic emergencies (i.e. traffic accidents) in specific areas, and (3) dynamic monitoring and managing the continuous use in public transit services (i.e. change in car lanes) that may lead to changes in macro traffic conditions. This article will use the above-mentioned functionalities of the Intelligent Traffic System as underlying simulative scenarios, to design and to implement a smart transportation system based on Internet of things and blockchain—both share inherent distributed technology characteristics—combining both Internet of things sensor nodes and distributed ledger technology, to (1) record the changes in intelligent transportation systems and (2) set up a credit-token mechanism for paying the use and misuses in public transit services accordingly. The Intelligent Traffic System described in this article is intended to be used as experimental project only, given the terms and conditions as depicted in the simulated scenario. In real-life traffic scenarios, it may generate more complex system and data security issues, which will be elaborated and analyzed at the end of this article. Intelligent Traffic System is a comprehensive smart system; it can significantly change and reinvent the wheel for traffic conditions. Based on the system development as discussed in this article, there are still a lot of demands and challenges that need to be addressed in the future. Such topic scope will be explored in depth in our subsequent research.

Adaptive packet scheduling in IoT environment based on Q-learning

In the internet of things (IoT) environment consisting of various devices the traffic condition dynamically changes. Failure to process them in complying with the QoS requirement can significantly degrade the reliability and quality of the system. Therefore, the gateway collecting the data needs to quickly establish a new scheduling policy according to the changing traffic condition. The traditional packet scheduling schemes are not effective for IoT since the data transmission pattern is not identified in advance. Q-learning is a type of reinforcement learning that can establish a dynamic scheduling policy without any prior knowledge on the network condition. In this paper a novel Q-learning scheme is proposed which updates the Q-table and reward table based on the condition of the queues in the gateway. Computer simulation reveals that the proposed scheme significantly increases the number of packets satisfying the delay requirement while decreasing the processing time compared to the existing scheme based on Q-learning with stochastic learning automaton. And the processing time is also minimized by omitting unnecessary computation steps in selecting the action in the iterative Q-learning operations.

TRAFFIC EVENT DETECTION USING TWITTER DATA BASED ON ASSOCIATION RULES

Abstract. Social media platforms allow millions of people worldwide to instantly share their thoughts online. Many people use social media to share traffic related experiences and events with online posts. A large amount of traffic related data can be obtained from these online posts – especially geosocial media data, where posts are tagged with geolocation information such as coordinates or place names. By extracting traffic events from geosocial media data, drivers can adapt to changing traffic conditions, while traffic management departments can propose timely and effective plans to improve traffic conditions. Most of the existing studies query traffic-related information based on a list of single keywords, which result in large amounts of noisy data – negative data containing one or more traffic-related keywords, but do not actually represent real-world traffic events. This paper aims to filter noisy data by mining association rules among words in positive data containing messages representing traffic events. Messages are more likely to be true traffic events if they follow the co-occurrence pattern of words mined from positive samples. A case study was conducted in Toronto, Canada using Twitter data. The tweets queried by the association rules were classified into non-traffic event, traffic accidents, roadwork, severe weather conditions, and special events with an 85% accuracy based on supervised machine learning methods. Compared with hourly average travel speed data, 81% of detected events were identified as real-world traffic events. This research sheds light on traffic condition monitoring in smart transportation platforms, which plays an important role for smart cities.

Short-Term Traffic Prediction for Edge Computing-Enhanced Autonomous and Connected Cars

Autonomous and connected cars (ACCs), together with edge computing (EC), have been recognized as a promising solution to achieve green intelligent transportation for smart cities. This paper aims to address short-term traffic prediction, a fundamental enabler for the success of ACC applications, under the European Telecommunications Standards Institute multi-access EC (MEC) architecture that exhibits constraints different from conventional cloud computing. First, a data-centric experiment platform is designed and implemented to facilitate traffic prediction algorithm development. This paper further proposes a novel short-term traffic prediction model that integrates a traffic light model and a vehicle velocity model, considering limited computing resources of MEC servers. We note that the effects of traffic lights are complicated and have not been rigorously examined in most, if not all, of the related work. This work models the queueing time when a driver arrives at a road intersection and faces a red light. Moreover, to forecast the vehicle velocity, we propose a novel low-complexity semiparametric prediction model considering periodic features and spatial/temporal correlations of dynamic road events. The experiment results demonstrate that our vehicle-velocity prediction model achieves almost equivalent accuracy to the well-known Long Short-Term Memory Neural Network model, requiring much lower computational complexity. Our experiment results also confirm that since our integrated model pays attention to the traffic-light effects and the individual driver behavior, it can more effectively capture the real-time changes of traffic conditions.

The study of the influence of intelligent driver assistance systems on the capacity of urban roads

The article gives theoretical and experimental studies of the time delay of cars in the stream, due to the time of the driver’s reaction to changing traffic conditions. In the present study, it was switching of allowing signal of the traffic light which was assigned as the initial moment of the measurements. The observation showed the dependence of the delay time of the car on its position in the queue at the junction with traffic light regulation. Due to the presence of the human factor, the accumulated delay along the flow has had a great influence on the number of vehicles which had the opportunity to cross the junction within one traffic light cycle. A situation was simulated in which all vehicles in a stream were equipped with an assistant driver device when moving in a traffic jam. The value of the delay of the vehicle in this case depends not on the actions of a single person, but on the electronics. It was found that the delay of the car when using electronics is 5.44 times less than the delay caused by the human factor. The study was conducted in a part of Nadezhda Suslova street in the city of Nizhny Novgorod and showed the possibility to increase the speed and intensity of the considered actual traffic flow.

Distance‐oriented hierarchical control and ecological driving strategy for HEVs

Hybrid electric vehicles (HEVs) provide a promising alternative to conventional engine-powered vehicles, with less emission and better fuel economy. This study proposes a hierarchical control for a power-split HEV over a driving cycle, featuring pre-trip optimisation and en-route speed adaption. Constraints including vehicle powertrain boundaries, road gradients, and speed limits, are taken into consideration. In the first stage, the HEV operating conditions, including the optimal vehicle SOC, speed profiles, and total driving time, are generated for the entire trip before departure. Based on the pre-trip results, the second stage adapts the vehicle speed for a short horizon when driving, while taking the safety spacing to the preceding vehicle into consideration, which acts as an indicator of actual traffic conditions and guarantees safe driving. Both optimisations are conducted under the distance domain for realising localisation in the optimal speed profile due to frequent changes in traffic conditions. An estimation of distribution algorithm is used to run the simulation so that the feasibility, robustness, and effectiveness of the proposed hierarchical control can be demonstrated.

Modeling Metropolitan-Area Ambulance Mobility Under Blue Light Conditions

Actions taken immediately following a life-threatening personal health incident are critical for the survival of the sufferer. The timely arrival of specialist ambulance crew in particular often makes the difference between life and death. As a consequence, it is critical that emergency ambulance services achieve short response times. This objective sets a considerable challenge to ambulance services worldwide, especially in metropolitan areas, where the density of incident occurrence and traffic congestion are high. Using London as a case study, in this paper, we consider the advantages and limitations of data-driven methods for ambulance routing and navigation. Our long-term aim is to enable considerable improvements to their operational efficiency through the automated generation of more effective response strategies and tactics. A key ingredient of our approach is to use a large historical dataset of incidents and ambulance location traces to model route selection and arrival times. Working on the London road network graph modified to reflect the differences between emergency and civilian vehicle traffic, we develop a methodology for the precise estimation of expected ambulance speed at the individual road segment level. We demonstrate how a model that exploits this information achieves best predictive performance by implicitly capturing route-specific persistent patterns in changing traffic conditions. We then present a predictive method that achieves a high route similarity score while minimising journey duration error. This is achieved through the combination of a technique that correctly predicts routes selected by the current LAS navigation system and our best performing speed estimation model. This hybrid approach outperforms alternative mobility models. To the best of our knowledge, this paper represents the first attempt to apply a data-driven methodology for route selection and the estimation of arrival times of ambulances travelling with blue lights and sirens on.

ДО ПИТАННЯ ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ СВІТЛОФОРНОГО РЕГУЛЮВАННЯ

Предметом вивчення в статті є новий підхід визначення параметрів руху транспортних засобів і особливості сучасної організації моніторингу транспортних потоків (ТП) на таких регульованих перехрестях. Метою є розробка нового способу, що дозволяє підвищити рівень якості та комплексної оцінки визначення елементів циклу світлофорного регулювання (СР) в умовах зміни параметрів руху ТП в зоні перехрестя. Завдання дослідження: аналіз існуючих підходів моніторингу ТП на регульованих перехрестях; розробка нового способу, що дозволяє отримати більш повну інформацію для контролю та ефективного визначення оптимальних значень часу дії елементів циклу СР; розробка способу, що відповідає вимогам універсальних адаптивних систем, що дозволяють ефективно реагувати на зміни умов руху ТП на перехресті. Результати. Розкрито деякі особливості сучасної організації моніторингу ТП на локальних об'єктах, а також проаналізовано їх основні переваги та недоліки. Представлений новий спосіб визначення елементів циклу СР в умовах динамічного зміни параметрів руху ТП і адаптивного реагування системи на дорожню ситуацію. Наведено варіант спрощеної структури пристрою для реалізації запропонованого способу і ефективної обробки вихідної інформації, показана достовірність, і працездатність алгоритмів, закладених в спосіб. Висновки. Ефективне визначення параметрів руху ТП в зоні перехрестя по кожній смузі руху протягом світлофорного циклу дає можливість отримати більш повний комплекс інформації для контролю і подальшого оперативного управління рухом на перехресті. Показано шляхи розширення функціональних можливостей запропонованого способу за рахунок одночасного визначення додаткових параметрів стану ТП на перехресті і критеріїв оцінки якості НГ. Гнучка технологія і комплексна оцінка режимів СР дозволяє в реальному масштабі часу здійснювати як параметричну адаптацію, так і структурну адаптацію за рахунок реорганізації схем управління руху.

Short-term traffic volume prediction by ensemble learning in concept drifting environments

Because of the rapid changes in traffic conditions caused by various circumstances, such as road construction and traffic jams, the distribution of the traffic volume data changes over time. The performances of traditional traffic volume prediction methods, with fixed model types and parameter settings, suffer from gradual degradation during these concept drift processes. In this paper, a novel incremental regression framework under the concept drifting environment is proposed, with ensemble learning as the major solution for updating the distribution representation. First, we transform the regression problem of traffic volume forecasting into a binary classification problem. Second, loss functions for incremental and ensemble learning are constructed based on this transformation. Finally, the incremental learning of the regression function is formulated as stepwise updating of the decision hyperplane. The experimental results show that our method is more stable and accurate than the existing incremental and ensemble regression methods.

Hierarchical Energy Management for Power-Split Plug-In HEVs Using Distance-Based Optimized Speed and SOC Profiles

This paper proposes a distance-based two-stage energy management strategy for power-split plug-in hybrid electric vehicles (PHEVs). One stage is for long-term optimization and the other is for short-term adaptation to actual traffic conditions. Energy consumption in PHEVs depends on the characteristics of the drivetrain as well as the operating conditions such as power demands and their split. Thus, prior to departure, the operating conditions for a whole trip are optimized for the drivetrain characteristics and trip information, which generates optimal speed and state-of-charge profiles. While driving, the operating conditions are adapted to current traffic conditions for a short horizon on the basis of long-term optimization results. In consideration of the changeability of traffic conditions, the proposed energy management strategy is performed in a distance domain, which localizes the effects of changes in traffic conditions on the long-term optimization results. Therefore, this distance-based two-stage strategy improves the balance between the optimality and the real-time computing time, which is suitable for online management. A model for the propulsion system in a PHEV and the energy management strategy were formulated in a distance domain. An estimation of distribution algorithm was used for long-term optimization and local adaptation.

Definição de segmentos homogêneos para o trecho da BR-040 que compreende a cidade de Congonhas/MG

As condições de trafegabilidade do pavimento de uma rodovia estão sujeitas às mais diversas variações ao longo de sua extensão; seja devido a características físicas; seja devido alterações no tráfego, no clima ou, em obras de revitalização. Dessa forma, muitos são os parâmetros considerados no desempenho do pavimento rodoviário, o que consequentemente, resultará em suas condições de trafegabilidade e, tornar-se-á, imprescindível, a consequente divisão da rodovia em segmentos homogêneos, os quais necessitam conter características estruturais e comportamentais semelhantes. Neste ínterim, este trabalho visa a elaborar uma nova proposta para segmentação homogênea da rodovia BR-040, trecho que compreende a cidade de Congonhas/MG, contribuindo assim com o processo de análise comportamental da rodovia. Para tanto, foram utilizadas informações do banco de dados da gerência de pavimentos da concessionária responsável pelo trecho entre Brasília/DF e Juiz de Fora/MG. A delimitação dos novos segmentos homogêneos foi determinada conforme o “Método das Diferenças Acumuladas” (Analysis Unit Delineation by Cumulative Differences), recomendado pela American Association of State Highway and Tranportation Officials – AASHTO (1993) e definida com base na estrutura do pavimento, bem como parâmetros que refletem as condições estruturais e funcionais dos pavimentos. Foi possível a identificação de 45 segmentos homogêneos, evidenciando que houve alterações significativas nas condições estruturais e funcionais do pavimento analisado.

Effects of vehicle restriction policies: Analysis using license plate recognition data in Langfang, China

To mitigate externalities of the urban transportation system, various travel demand management policies have been tested across the world in the past decades. Among them, vehicle restriction is considered the most straightforward way to reduce travel demands. The effects of this policy, however, are still inconclusive. Using the massive license plate recognition (LPR) data in Langfang, China, this study investigates the differences of two typical license-plate-number-based vehicle restriction strategies, i.e., One-Day-Per-Week (ODPW) and Odd-And-Even (OAE). This study compares ODPW and OAE from two perspectives: travel demand and traffic condition over the road network. In addition to the change of traffic volume, the analysis of travel demand also includes the investigations of “illegal” travel, which denotes the road use of restricted vehicles, and travel intensity, which represents the travel frequency of certain vehicles in a time period. The changes of traffic condition are assessed by travel speed. Findings confirm that traffic volume declines as ODPW shifts to OAE, but not as significantly as expected. This less-than-full adjustment can be explained by the increase of illegal travel and travel intensity. The policy shift from ODPW to OAE significantly improves the travel speed in most of the road segments, whether in peak hours or off-peak hours. The conclusions in this study provide in-depth insights into the influence of vehicle restriction policies and contribute to the effective design of travel demand management strategies.

Green Swarm: Greener routes with bio-inspired techniques

This article proposes a mobility architecture, called Green Swarm, to reduce greenhouse gas emissions from road traffic in smart cities. The traffic flow optimization of four European cities: Malaga, Stockholm, Berlin, and Paris, is addressed with new case studies importing each city's actual roads and traffic lights from OpenStreetMap into the SUMO traffic simulator, so as to find the best ways to redirect the traffic flow, and advise drivers. Additionally, the proposal is compared with three other strategies, which are also combined with Green Swarm in order to improve metrics such as travel times, gas emissions, and fuel consumption. This results in reductions in gas emissions as well as in travel times and fuel consumption in more than 500 city scenarios. The proposal has also been tested in scenarios where not all drivers are using it, to observe the change in traffic conditions when it is only in partial use, successfully paving the way for future sustainable cities.