Congested Traffic Conditions Research Articles

MRGM: An Adaptive Mechanism for Congestion Control in Smart Vehicular Network

Traffic flow on roads has increased manifolds from past few decades due to increase in number of vehicles and rise in population. With fixed road infrastructure and more vehicles on traffic routes lead to traffic congestion conditions especially in urban areas of developing nations. Traffic jams are normal in major cities which ultimately cause delay in travel time, more fuel consumption and more pollution. This manuscript propose a Multi-metric road guidance mechanism(MRGM) which considers multiple metrics to analyze the traffic congestion conditions and based on the conditions effective optimal routes are suggested to the vehicles. The Simulation of the proposed mechanism is performed with the SUMO by using the python script and the results show that proposed mechanism i.e MRGM outperforms other mechanism in terms of traffic efficiency, travel time, fuel consumption and pollution levels in the smart vehicular network.

Development of Online Vissim Traffic Microscopic Calibration Framework Using Artificial Intelligence for Cairo CBD Area

This paper makes a notable contribution to Transportation Planning in urban areas by considering the application of a key transportation Planning software package for the traffic conditions in the Central Business District of Cairo. For urban areas with heterogeneous and very congested traffic conditions and uneven driving behavior such development can be very useful. The paper shows how a microscopic simulation model using a Multilayer Feedforward neural network (MFNN) to calibrate online the VISSIM package driving parameters’ values based on predictions of travel time and traffic flow on the network elements. The two-step calibration procedure is faster and more applicable for on-line models than the approaches followed in current literature that require time-consuming iterations for model calibration. Also, this research uses combined Artificial Intelligence models (Long-Short Term Memory based Recurrent Neural Networks - LSTM-RNNs) and Multilayer Feedforward neural network (MFNNs) and calibrates them based on the driving behavior and traffic condition on successive time intervals. In this way, the prediction of the future traffic condition is based on actual traffic conditions on the past intervals and the actual driving behavior.

GIS and Gravity Model-Based Accessibility Measure for Delhi Metro

Metro is a rapid transit mode that helps in fast and efficient movement of the public. To make metro journey more convenient and attractive, from origin to destination, accessibility of metro must be good. In this study, tri-level analysis is carried out for the study area of Delhi. Traffic congestion and environmental condition of Delhi are degrading continuously, and to improve the situation, a sustainable Multi Modal Transport System (MMTS) with high accessibility must be explored. In this paper, policy to improve zone to zone metro accessibility for work purpose is discussed and the relation between multimodal transport system with accessibility is studied using GIS (ArcGIS), macrosimulation software (VISUM) and gravity model. It was observed that by the addition and extension of metro lines, transport accessibility is greatly improved. This study shows that percentage of zones in Delhi with higher accessibility level could increase from 5 to 13.88% with the implementation of phase IV of metro. In addition, the implementation of phase IV metro could reduce the average travel time between zones and metro stations. It will also reduce the travel time between metro stations substantially (for the zones covered in this study the average travel time is reduced by 23.63% and for the station covered in this study the average travel time is reduced by 17.73%). Further, it was found that ‘out vehicle travel time’ (OVTT) is having a major impact on the accessibility of metro. Lastly, it was concluded that with the higher level of metro accessibility, people will prefer the multimodal transport system with metro as the main mode. Travel time isochrones for public and private trips for the estimated MMTS model are also presented in this paper to depict the changes in accessibility for different scenarios. This will further help in improving private transport accessibility by reducing congestion on roads. Thus, planning for transport accessibility helps in developing the city in a sustainable manner in the future.

Internet-of-Video Things Based Real-Time Traffic Flow Characterization

Real-world traffic flow parameters are fundamental for devising smart mobility solutions. Though numerous solutions (intrusive and non-intrusive sensors) have been proposed, however, these have serious limitations under heterogeneous and congested traffic conditions. To overcome t

A link-to-link segment based metamodel for dynamic network loading

Dynamic network loading (DNL) is a core part of simulation-based dynamic traffic assignment (DTA) models, which describes how traffic propagates on a transportation network and usually maps a set of route departure rates to a set of route travel times. Due to the incorporation of different intra- and inter-link dynamics such as queuing, spillback, etc., DNL becomes complex, resulting in models with undesirable analytical properties (e.g., discontinuity, non-differentiability, and lack of closed-form) and requiring intensive computational efforts. Moreover, as DNL is applied repeatedly in simulation-based DTA algorithms, it often is the computational bottleneck of large-scale traffic assignment and related optimization problems. In this research, we address these problems by proposing a link-to-link segment-based Kriging metamodel for dynamic network loading. The proposed Kriging model calculates route travel time, link travel time, and link flow. It systematically approximates the traditional DNL model while being faster and maintaining some desirable analytical properties. Both temporal and spatial proximity information and the route segment incidence information are incorporated in the metamodel formulation. The models are trained using maximum likelihood estimation with different dataset sizes. In the experiments, the proposed Kriging metamodels performed better than a state-of-the-art neural network model, achieving an average error of 1.64% and 5.32% in the Nguyen Dupuis (ND) network and the Sioux Falls (SF) network, respectively, along with at least 43 times, sometimes as high as over 1300 times, improvement in computational speed. The results, however, suggest a tradeoff between efficiency and accuracy and the size of the training dataset. With balanced route demand, the model’s performance was reasonable even under congested traffic conditions. The MAPE values were at most 3% and 18% in the ND network and the SF network, respectively, for different level of congestion demonstrating the effectiveness of the proposed model.

Bus arrival time prediction and reliability analysis: An experimental comparison of functional data analysis and Bayesian support vector regression

To maintain the stability and punctuality of bus systems, an accurate forecast of arrival time is essential to devise control strategies to prevent bus bunching especially under congested traffic conditions. Transit agencies provide travelers with accurate and reliable bus arrival times to downstream stations to improve transit service quality so as to attract more transit riders. Varieties of approaches have been dedicated to providing high prediction accuracy while the measure of the associated uncertainty is ignored. Noting that the quantification of uncertainty is vital for robust performance, this paper proposes data-driven approaches based on the Functional Data Analysis (FDA) and Bayesian Support Vector Regression (BSVR) for short-term bus travel time prediction while anticipating various uncertainties. To capture spatial–temporal dynamic traffic conditions along the route so as to increase the accuracy of the journey time prediction and to capture the skewness in journey time distribution, a probabilistic nested delay operator is adopted. Journey time reliability analysis is then conducted using the skewness of dynamic journey time distribution. An empirical study is carried out by fusing the bus transit date of No. 261 bus route and Floating Car Data (FCD) in Guangzhou. The proposed FDA and BSVR methods applied in conjunction with the probabilistic nested delay operator turn out to be highly competitive when performing forecasts under various traffic conditions. Comparative studies indicate that FDA provides more accurate prediction results and tends to anticipate uncertainties in journey time distribution more effectively.

Fuel economy valuation and preferences of Indian two-wheeler buyers

India has experienced exponential growth in sales of two-wheelers due to their high fuel economy and easy manoeuvrability in congested traffic conditions. Understanding Indian consumers’ valuation of fuel economy while purchasing a two-wheeler is crucial to evaluate whether Indian two-wheeler buyers are myopic with regards to future fuel costs. However currently, there exists no quantification of the value that Indian two-wheeler buyers ascribe to fuel economy. We address this gap in the literature by analyzing revealed preference survey data from more than 8000 respondents across India, who purchased a new two-wheeler in 2018. Discrete choice models, including standard conditional logit and flexible mixed logit, are used to infer estimates of the discount rate that Indian consumers apply to obtain the present value of future operating cost at the time of two-wheeler purchase. The results of conditional logit indicate that the annual discount rate is below 10% for Indian two-wheeler buyers with monthly household incomes above 15,000 rupees (∼US$215 in 2018; ∼73% of the sample). Mixed logit suggests substantial heterogeneity in discount rates across respondents at a given income level. The median of the mixing distribution is close to the conditional logit estimates, which leads to a conclusion that an average Indian two-wheeler buyer ascribes a high value to future fuel cost savings. Besides, our analysis provides various insights into Indian consumers’ preferences. For instance, style/looks, fuel economy, comfort, and brand (in decreasing order) are the top four factors that influence two-wheeler purchase decisions of Indian consumers.

Road Boundary-Enhanced Automatic Background Filtering for Roadside Lidar Sensors

The roadside-deployed lidar sensor provides a solution to obtain the real-time, high-resolution micro traffic data (HRMTD) of all road users in the mixed traffic situation (connected vehicles and unconnected vehicles both exist on the roads). Background filtering is a necessary and important step for the HRMTD collection to serve the connected vehicle. Without excluding the background points, the accuracy of Euclidean-based object clustering and tracking algorithms can be reduced. The widely used method-3D-density-statistic filtering (3D-DSF) for roadside lidar background filtering can effectively exclude the background points for free-flow conditions. However, the performance of 3D-DSF can be greatly influenced by congested traffic conditions. This article presents a revised 3D-DSF algorithm to automatically extract background points by involving the road-boundary information. This new method is named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">road-boundary-enhanced, 3D-density statistic filtering</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3D-DSFRB</i> ). This algorithm involves the boundary of the historical trajectories of road users as the region of interest (ROI) to enhance the accuracy of background filtering. A revised grid-based method was developed for road-boundary ID. The 3D-DSF was only applied for the area outside of the ROI. Within the ROI, only ground surface was excluded. Case studies were conducted to evaluate the effectiveness of the 3D-DSFRB algorithm. The results showed that the 3D-DSFRB can filter background points for both free-flow conditions and congested traffic conditions. The time cost of the 3D-DSFRB was also reduced compared to the 3D-DSF. Compared to the state of the art, the 3D-DSFRB improved the accuracy of background filtering.

Enhancement of idling characteristics using multi-objective approach in light-duty diesel Vehicle fuelled with orange peel biofuel

Along with an increasing demand for an alternate source of fossil fuel, rapid urbanization has increased the motor vehicle pollutant spews in a metropolis, especially in traffic congestion or cold start condition. In reference to these situations; this research is aimed at utilizing orange peel oil (OPO) as blend fuel to mineral diesel in light-duty diesel engine and asses the output characteristics for optimizing engine fuel map under idling condition. A four-factor central composite design (CCD) approach-based response surface methodology (RSM) analysis is applied to statistically relate the effect of important input variables on key engine characteristics to achieve minimum emissions without a major compromise on the power output. From the desirability report, for orange Peel Oil (20%) blended with diesel fuel (80%) (OPO20) fuel, the optimum pilot injection timing is 210 before top dead centre (bTDC), pilot mass of 24% and apt fuel injection pressure is 500 bar while maintaining 1100 rpm as idle engine speed. While tuning the OPO20 operated engine fuel map under this optimum operating condition, a notable reduction of 34% in carbon monoxide and 36.84% reduction in smoke is observed in comparison to diesel. However, the increased mean gas temperature in-turn translated into 37.65% increase in oxides of nitrogen. As a trade-off to the reduced emissions, a 15.47% decrease in the brake thermal efficiency is observed while the specific fuel consumption also increased by 29.98% in comparison to diesel operation.

The Traffic Assessment of the Adjacent Arterial Streets Close to Gates of the Main Campus of the University of Mosul, Iraq

Like many university campuses, University of Mosul Main Campus (UOMMC) a major activity center and focus of traffic generator. The UOMMC surrounded by segments of four major arterial streets that form the outer limits of the campus At segments close to the campus gates of these streets, many traffic problems and congestion conditions occur due to the significant traffic volumes and movements with high vehicles and pedestrians conflicts, frequent parking turnover, increasing passengers loading and unloading operations throughout the campus working days. The main objective of this study is to evaluate the current and future traffic congestion situation at the main campus gates and its effect on the multilane streets segments around the main campus, near its gates. The methods of data collection included site visits, personal observations, questionnaire survey, measurements, and imaging techniques. The current and future traffic operational state of these segments concerning the traffic stream characteristics or level of service is defined by three primary measures (according to HCM 2016): traffic flow rate, traffic speed, and traffic density. Finally, the study provided suggested and proper solutions for the existing and future traffic problems simulate the benefit of the campus community to provide the protection of life and property, optimum use of campus gates and, acceptable and safe traffic flow on the multilane streets around the main campus.

Lane-Free Artificial-Fluid Concept for Vehicular Traffic

Vehicular traffic has evolved as a crucial means for the transport of persons and goods, and its importance for the economic and social life of modern society cannot be overemphasized. On the other hand, recurrent vehicular traffic congestion, which appears on a daily basis, particularly in metropolitan areas, around the globe, has been a (increasingly) serious, in fact threatening, problem that calls for drastic solutions. Traffic congestion causes excessive travel delays, substantial fuel consumption and environmental pollution, and reduced traffic safety. Conventional traffic management measures are valuable [1]-[3] but not sufficient to address the heavily congested traffic conditions, which must be addressed in a more comprehensive way that exploits gradually emerging and future ground-breaking capabilities of vehicles and the infrastructure.

STUDI EVALUASI TITIK KEMACETAN SIMPANG EMPAT DI JALAN KEBANGKITAN NASIONAL - JALAN BHAYANGKARA KOTA SURAKARTA

The city of Surakarta is a big city that has a function as a center for trade, tourism, culture, education, and sports which always experiences an increase in population and number of vehicles every year. The increasing number of facilities and infrastructure needs in the transportation sector must of course be balanced with the existence of supporters that make these transportation facilities more useful, namely the existence of roads and their management and performance of intersections. The intersection area with a signaling the National Awakening Road - Jalan Bayangkara is in a commercial area because there are many shops and settlements, so it has heavy traffic and a fast growth rate of traffic. This study aims to provide an overview of traffic congestion conditions that occur at the intersection of the four-lane National Awakening Street - Bhayangkara Street, Surakarta city and analyze the right solution to be applied in order to overcome congestion that occurs at the four-way intersection of Jalan Kebangkitan Nasional - Jalan Bhayangkara, Surakarta. From the results of existing calculations for the intersection without Signal Jl. National Awakening - Jl. Bhayangkara Kota Surakarta with the MKJI 1997 method, the value of the degree of saturation (DS) is obtained, namely = 1.12 or more than 0.85. So that we need several alternatives to describe congestion at the intersection. For the first alternative using the method of switching the road into one direction. The results of the degree of saturation (DS) = 0.32 for the Western approach, DS = 0.71 for the South approach. Then we also try to make a second alternative, which is to make the intersection a signaled intersection. So the value of the degree of saturation (DS) is = 0.47 for the north approach, DS = 0.73 for the south approach, DS = 0.63 for the eastern approach and DS = 0.73 for the western approach. Keywords: Intersection, intersection with signal, one direction, degree of saturationThe city of Surakarta is a big city that has a function as a center for trade, tourism, culture, education, and sports which always experiences an increase in population and number of vehicles every year. The increasing number of facilities and infrastructure needs in the transportation sector must of course be balanced with the existence of supporters that make these transportation facilities more useful, namely the existence of roads and their management and performance of intersections. The intersection area with a signaling the National Awakening Road - Jalan Bayangkara is in a commercial area because there are many shops and settlements, so it has heavy traffic and a fast growth rate of traffic. This study aims to provide an overview of traffic congestion conditions that occur at the intersection of the four-lane National Awakening Street - Bhayangkara Street, Surakarta city and analyze the right solution to be applied in order to overcome congestion that occurs at the four-way intersection of Jalan Kebangkitan Nasional - Jalan Bhayangkara, Surakarta. From the results of existing calculations for the intersection without Signal Jl. National Awakening - Jl. Bhayangkara Kota Surakarta with the MKJI 1997 method, the value of the degree of saturation (DS) is obtained, namely = 1.12 or more than 0.85. So that we need several alternatives to describe congestion at the intersection. For the first alternative using the method of switching the road into one direction. The results of the degree of saturation (DS) = 0.32 for the Western approach, DS = 0.71 for the South approach. Then we also try to make a second alternative, which is to make the intersection a signaled intersection. So the value of the degree of saturation (DS) is = 0.47 for the north approach, DS = 0.73 for the south approach, DS = 0.63 for the eastern approach and DS = 0.73 for the western approach. Keywords: Intersection, intersection with signal, one direction, degree of saturation

Intelligence in traffic simulation model: Modeling congested network

Traffic simulation tools are becoming more popular as complexity and intelligence are growing in transportation systems. The need for more accurate and intelligent traffic modeling is increasing rapidly as transportation systems are having more congestion problems. Although traffic simulation models have been continuously updated to represent various traffic conditions more realistically, most simulation models still have limitations in overcapacity congested traffic conditions. In traditional traffic simulation models, when there is no available space due to traffic congestion, additional traffic demand may never be allowed to enter the network. The objective of this paper is to investigate one possible method to address the issue of unserved vehicles in overcapacity congested traffic conditions using the VISSIM trip chain. The VISSIM trip chain is used for this analysis as it has the advantage of holding a vehicle without eliminating it when traffic congestion prevents its entrance onto a network. This will allow the vehicle to enter when an acceptable gap becomes available on the entry link. To demonstrate the difference between the simulation using standard traffic input and the trip chain method, a sample congested traffic network is built and congested traffic scenarios are created. Also, simulations with different minimum space headway parameters in the priority rules are analyzed to model congested traffic conditions more realistically. This will provide the insight about the sensitivity of the model to this parameter. Based on the analysis conducted it is concluded that, with appropriate calibrations, the trip chain feature in VISSIM has the potentials to be useful in modeling overcapacity congested traffic conditions more realistically.

Reliable Urban Transportation Network Design Problem Considering Recurrent Traffic Congestions

Traffic congestion is one of the main reasons for the unsustainability of an urban transportation network. Changes in travel demand and streets’ capacity lead to traffic congestion in urban transportation networks, which is known as recurrent traffic congestion. This study aims to assess the performance reliability of urban transportation networks subject to recurrent traffic congestion conditions in order to help travelers to find alternative non-congested routes. A non-congested route is a route without any congested link. The network reliability is defined and modeled as two different scenarios; users’ unawareness of the network’s traffic congestion and users’ ongoing awareness of the network’s traffic congestion. In addition, a reliable network design model is provided to optimize the reliability of the network taking into account street widening policy and budget constraints. Lastly, a Quantum-Inspired Evolutionary Meta-Heuristic Algorithm is adopted; while maintaining accuracy, to reduce problem-solving time and providing the possibility of solving large-scale problems for real networks. To show the applicability of the proposed models and algorithm, they have been implemented on the Sioux Falls transportation network. The results indicate users’ awareness of traffic congestion in the network increases its reliability, and centrally located links are the first candidates for street widening.

A DVB-S-Based Multichannel Passive Radar System for Vehicle Detection

Vehicle detection systems play a significant role in intelligent transportation systems (ITSs). At present, vehicle detection is realized either by very costly inductive loops, by video cameras that require good weather conditions and high computing resources, or by recently developed magnetic sensors that only obtain limited detection accuracy in congested traffic conditions. To overcome these challenges, this paper develops a passive radar system that is robust to bad weather conditions, low cost and radio silent for vehicle detection. The device is installed at the side of the road under crowded traffic conditions. In the developed system, cross-correlation (CC) operation is applied to obtain suboptimal detection gain and computational complexity. To decrease the impact of interference that is independently and identically distributed (i.i.d.) across isolated frequency channels and that of the Doppler effect produced by a mobile vehicle, a multichannel joint detection scheme based on multichannel fusion (MCF) is proposed and implemented. Furthermore, to achieve high resolution, an illuminator of opportunity (IO) with wide bandwidth is selected, and the system is then designed to receive a digital video broadcast-satellite (DVB-S) signal with a bandwidth of 37.5 MHz over multiple channels simultaneously. Simulation results show that the proposed MCF achieves better performance than the single-channel-based CC, and the advantage can be enhanced by increasing the number of channels for joint detection. Field test results further verify the novelty of the MCF and the developed passive radar prototype.

Studying perturbations and wave propagations by lane closures on traffic characteristics based on a dynamic approach

Lane closures cause perturbations and wave propagations on traffic characteristics of freeways and consequently affect traffic flow. To conduct a dynamic analysis regarding the effects of lane closures on traffic characteristics, this study aimed to examine the effects of lane closures as multi-static bottlenecks on the evolution and wave propagation of uniform traffic flow in a four-lane divided freeway based on the field study and macroscopic traffic flow model. One-lane closure is a work zone and two-lane closure occurs when the second lane near the one-lane closure is blocked due to an incident caused by a lane change. In addition, the classical Lighthill–Whitham–Richards (LWR) model was combined with the non-linear Greenshield’s speed–density relation to expand the models for characterizing the behavior of shockwaves under lane closures. Thus, based on numerical procedures regarding the Lax–Friedrichs scheme, the results indicated that wave propagations by two-lane closure have significant effects on the perturbations of traffic flow density which are 1.5 times as much as the wave propagations in one-lane closure. Moreover, one-lane closure reduced the capacity by 21% although the two-lane closure caused a 45% decrease in the capacity. A comparison between expanded LWRρ and LWRu models with the LWR model demonstrated that expanded models under two-lane closure and one-lane closure improved LWR model for the congested condition, and between uncongested and congested traffic condition 15.61% and 39%, respectively. Therefore, expanded models revealed a better performance as a dynamic approach to analyze the behavior of wave propagations in jam traffic conditions compared to LWR model under lane closures.

Edukasi Keselamatan Lalulintas Warga Ngadinegaran Kecamatan Mantrijeron Daerah Istimewa Yogyakarta

The community service program is carried out in Ngadinegaran, Mantrijeron, DIY. Mantrijeron Urban Village is in the Mantrijeron sub-district area with an area of ​​2.61 km2. Kampung Ngadinegaran is part of the tourist center in the city of Yogyakarta, with high population and during holidays the road conditions also become very crowded and even congestion often occurs. This highly congested traffic condition often causes accidents due to lack of knowledge of traffic safety. The target of this activity is to improve understanding, awareness and knowledge of traffic safety, so that the number of accidents that occur will be reduced. The method in this commuty service is to provide socialization about the importance of traffic safety . The result of socialization shows that the community is very interested in this activity and they want to get more information about traffic safety education regularly because this activity can improve the awareness about traffic safety.

Travel Time Prediction for Congested Freeways With a Dynamic Linear Model

Accurate prediction of travel time is an essential feature to support Intelligent Transportation Systems (ITS). The non-linearity of traffic states, however, makes this prediction a challenging task. Here we propose to use dynamic linear models (DLMs) to approximate the non-linear traffic states. Unlike a static linear regression model, the DLMs assume that their parameters are changing across time. We design a DLM with model parameters defined at each time unit to describe the spatio-temporal characteristics of time-series traffic data. Based on our DLM and its model parameters analytically trained using historical data, we suggest an optimal linear predictor in the minimum mean square error (MMSE) sense. We compare our prediction accuracy of travel time for freeways in California (I210-E and I5-S) under highly congested traffic conditions with those of other methods: the instantaneous travel time, k-nearest neighbor, support vector regression, and artificial neural network. We show significant improvements in the accuracy, especially for short-term prediction.

MRGM: An Adaptive Mechanism for Congestion Control in Smart Vehicular Network

Traffic flow on roads has increased manifolds from past few decades due to increase in number of vehicles and rise in population. With fixed road infrastructure and more vehicles on traffic routes lead to traffic congestion conditions especially in urban areas of developing nations. Traffic jams are normal in major cities which ultimately cause delay in travel time, more fuel consumption and more pollution. This manuscript propose a Multi-metric road guidance mechanism(MRGM) which considers multiple metrics to analyze the traffic congestion conditions and based on the conditions effective optimal routes are suggested to the vehicles. The Simulation of the proposed mechanism is performed with the SUMO by using the python script and the results show that proposed mechanism i.e MRGM outperforms other mechanism in terms of traffic efficiency, travel time, fuel consumption and pollution levels in the smart vehicular network.

Identifying Critical Clusters of Traffic-Loading Events in Recurrent Congested Conditions on a Long-Span Road Bridge

This paper examines the nature of traffic loading in recurrent congested traffic conditions on a long-span suspension bridge. Traffic flow and percentage of trucks are extracted from image data and a cluster analysis performed to classify the data into four clusters. One cluster (MTHF, medium truck percentage and high flow) is identified that incorporates almost 50% of the hours of traffic data scattered throughout the day. Site-specific load assessment confirms that this MTHF cluster is the most critical for the bridge considered, the Forth Road Bridge in Scotland. For non-recurrent congestion, another cluster (HTLF, high percentage of trucks and low flow) is shown to govern but this finding is highly site-specific, depending on the relative frequency of the different types of congestion. A comparison of the maximum hourly/daily MTHF load effect of the cable force for five notional bridges shows that a 100% increase in the bridge span generates an increase of about 65% in the characteristic load effect.