Traffic Congestion Problems Research Articles

Traffic Congestion Prediction Based on Multivariate Modelling and Neural Networks Regressions

Smart traffic congestion reduction is actually a real challenge for big cities. Machine learning algorithms can play a significant role in traffic analysis, congestion prediction, and rerouting. In this paper, we propose a new prediction approach to reduce the traffic congestion problem by studying a scheme for predicting traffic flow information using four machine learning techniques: Feed Forward Neural Networks (FFNN), Radial Basis Function Neural Networks (RBFNN), simple linear regression model, and polynomial linear regression model. This prediction scheme is based on the following parameters: the average waiting time at entry and exit street pairs, the days of the week, hours of movement, holidays, and the rain rate. The results indicate that the FFNN technique overcomes the other techniques producing 97.6% prediction accuracy.

Fuzzy logic and deep Q learning based control for traffic lights

Traffic congestion is a major concern for many metropolises. Although it is difficult to regulate traffic flow because of numerous complexities and uncertainties, the traffic congestion problem must be mitigated in order to reduce the environmental problems related to traffic and the time lost on the roads in big cities. Intelligent traffic control methods, the use of which is increasing with the development of new methods, as opposed to conventional methods, and provide more efficient solutions, especially in traffic intersections with high traffic density. In this paper, we propose a new agent-based Fuzzy Logic assisted traffic light signal timing for traffic intersections. Deep Q-Learning algorithms and Fuzzy Logic Control (FLC) are used together in the proposed method. In this study, the proposed method and many traffic light control methods in the literature were simulated. In order to demonstrate the effectiveness of the proposed method, some of the important metrics of evaluation such as traffic congestion, air pollution, and waiting time were used in the assessment of the simulation results. In addition, with the proposed method, it has been shown that the stability and robustness of the system are increased.

Understanding sudden traffic jams: From emergence to impact

Road traffic jams are a major problem in most cities of the world, resulting in massive delays, increased fuel wastage, and monetary and productivity losses. Unlike conventional computer networks, which experience congestion due to excessive traffic, road transportation networks can experience traffic jams over prolonged periods due to traffic bursts over short time scales that push the traffic density beyond a threshold jam density. We observe that the emergence of such jams can happen over a very short duration, hence we term them as sudden traffic jams. We provide a formalism for understanding the phenomena of sudden traffic jams and show evidence of its existence using loop detector data from New York City. Further, we show the signature of sudden jams when observed at hourly resolution. We also provide a method to compute the traffic curve in a situation where we do not have access to fine-grained flow and density information. With this method, using only hourly speed data from Uber, we compute traffic curves for the road segments in Nairobi, São Paulo, and New York City, which is, by our knowledge, the first attempt to do so for signalized road networks. Running our analysis on the Uber movement speed data for the three cities, we show numerous instances of jams that last several hours, and sometimes as long as 2–3 days. Empirically, we find that Nairobi experiences 3x the mean jam time per road segment as compared to São Paulo and New York City. Based on key development metrics, we find that the ratio of traffic load per road segment for São Paulo, New York City, and Nairobi is approximately 1:2:3. We propose that chaotic driving patterns and traffic mismanagement in the developing world cities lead to tighter traffic curves, more intense jams and overall lower road capacity utilization, which explains the observed data. We posit that the problem of traffic congestion in developing countries cannot be solved entirely by building new infrastructure, but also requires smart management of existing road infrastructure.

Exploring the Role of Attitudinal Factors in Electric Vehicle Timeshare Rentals Adoption

Electric vehicle timeshare rentals (EVTRs) have been recognized as promising solutions to growingly severe problems of traffic congestion, air pollution, and insufficient parking spaces. This study aims to explore the factors that affect the adoption of EVTRs. To achieve the research objective, the household survey is conducted to obtain the travelers’ attitudes towards their travel. Ten latent attitudinal factors are extracted based on the technology acceptance model (TAM) and the theory of planned behavior (TPB). The multi-index and multi-cause (MIMIC) method simultaneously estimates the correlations between the attitudinal factors. Two logit models with attitudinal factors or not are constructed to estimate the quantitative relationship between various factors and EVTR adoption. The results show that the accuracy of the mixed logit model with latent attitude variables is better than the binary logit model without attitude latent variables. This indicates that attitude latent factors could be well matched with the traveler’s travel behavior and could better reflect travelers’ travel demand. Perceived comfort, perceived efficient, subjective evaluation, use preference, and use willingness significantly impact EVTR use frequency. The inconvenience in travelers, rental stations, shared vehicles, and use modes have significant negative impacts. Finally, social pressure has no significant impact. Findings provide valuable insights regarding the efficient planning of the EVTR system and allow decision-makers to develop scientific and practical measures of EVTRs.

Modeling the Operation of Signal-Controlled Intersections with Different Lane Occupancy

In many cities of the world, the problem of traffic congestion on the roads remains relevant and unresolved. It is especially noticeable at signal-controlled intersections, since traffic signalization is among the most important factors that reduce the maximum possible value of the traffic flow rate at the exit of a street intersection. Therefore, the development of a methodology aimed at reducing transport losses when pedestrians move through signal-controlled intersections is a joint task for the research and engineering community and municipalities. This paper is a continuation of a study wherein the results produced a mathematical model of the influence of lane occupancy and traffic signalization on the traffic flow rate. These results were then experimentally confirmed. The purpose of this work is to develop a method for the practical application of the mathematical model thus obtained. Together with the obtained results of the previous study, as well as a systems approach, traffic flow theory, impulses, probabilities and mathematical statistics form the methodological basis of this work. This paper established possible areas for the practical application of the previously obtained mathematical model. To collect the initial experimental data, open-street video surveillance cameras were used as vehicle detectors, the image streams of which were processed in real time using neural network technologies. Based on the results of this work, a new method was developed that allows for the adjustment of the traffic signal cycle, considering the influence of lane occupancy. In addition, the technological, economic and environmental effects were calculated, which was achieved through the application of the proposed method.

Road traffic analysis on the congestion problem using the Ford-Fulkerson algorithm

In this paper, the well-known Ford-Fulkerson algorithm in graph theory is used to determine the maximum flow in a road traffic network. Road traffic congestion is a major urban transport problem that occurs when the volume of traffic exceeds the capacity of existing road facilities. The manifestation of traffic congestion is due to the ownership of a high number of vehicles at the expense of road traffic infrastructure and the high growth of the urban population. Only one road connects the city center of Kinshasa, which concentrates the main activities of the inhabitants, to the international airport of Ndjili. Whether you are a pedestrian, a car driver or a public transport user, you cannot escape the traffic jams that refuse to leave Kinshasa. Even motorbike taxis cannot escape. Yet the grade separations built on Lumumba Boulevard in Debonhomme, at the Marché de la Liberté and at Pascal are all open to traffic. Built to facilitate the flow of traffic between the city center (Gombe) and N'djili International Airport, these elephantine structures serve little purpose. In this study, the identification of the maximum flow and bottleneck path along the Lumumba Boulevard between Debonhomme and Ndjili International Airport in the Tshangu district of Kinshasa was carried out. All possible routes from the different sources to the different wells were established. It emerged from this study that the optimal solutions would be the construction of secondary roads and improvement of the road facilities to minimize the problem of traffic congestion.

Study on Identification and Prevention of Traffic Congestion Zones Considering Resilience-Vulnerability of Urban Transportation Systems

In order to solve the problem of urban short-term traffic congestion and temporal and spatial heterogeneity, it is important to scientifically delineate urban traffic congestion response areas to alleviate regional traffic congestion and improve road network efficiency. Previous urban traffic congestion zoning is mostly divided by urban administrative divisions, which is difficult to reflect the difference of congestion degree within administrative divisions or traffic congestion zoning. In this paper, we introduce the Self-Organizing Feature Mapping (SOFM) model, construct the urban traffic congestion zoning index system based on the resilience and vulnerability of urban traffic systems, and establish the urban traffic congestion zoning model, which is divided into four, five, six, and seven according to the different structures of competition layer topology. The four vulnerability damage capacity indicators of traffic volume, severe congestion mileage, delay time and average operating speed, and two resilience supply capacity indicators of traffic systems, namely, road condition and number of lanes, are used as model input vectors; the data of Guiyang city from January to June 2021 are used as data sets to input four SOFM models for training and testing and the best SOFM model with six competitive topologies is constructed. Finally, the Support Vector Machine (SVM) is used to identify the optimal partition boundary line for traffic congestion. The results show that the four models predict the urban traffic congestion zoning level correctly over 95% on the test set, each traffic congestion zoning evaluation index in the urban area shows different obvious spatial clustering characteristics, the urban traffic congestion area is divided into six categories, and the city is divided into 16 zoning areas considering the urban traffic congestion control types (prevention zone, control zone, closure control zone). The spatial boundary is clear and credible, which helps to improve the spatial accuracy when predicting urban traffic congestion zoning and provides a new methodological approach for urban traffic congestion zoning and zoning boundary delineation.

Pricing schemes of simultaneous optimization of congestion and air pollution: a case study in Iran.

The problem of traffic congestion and the environmental issues related to air pollution are among the essential problems of urban management that metropolitan cities are trying to mitigate. Given that the contribution of motor vehicles to air pollution is significant, both goals are achieved by managing urban transport. Among the various methods of travel demand management, congestion pricing is a very efficient measure. This study tried to simultaneously increase the efficiency of the transportation network and reduce the environmental effects by using a bi-level model for the multi-modal network. For this purpose, the upper-level model minimizes the objective function, i.e., pollution emission costs and overall commuting costs. The lower level also has a transportation network model that provides the condition of user equilibrium. The genetic and Frank-Wolfe algorithms have been used to solve the bi-level programming model. Two pricing schemes, cordon-based and link-based, are used to investigate and assist policymakers. The proposed algorithm is also applied to a real-world road network in Isfahan, Iran. The results of the proposed models for different pricing strategies were compared. According to the results, both pricing schemes mitigate traffic congestion and pollution, although the reduction in pollution outside the cordon is less than inside. Demand has also shifted from the private car mode to public transportation by an average of 15%. However, link-based pricing provides better performance than cordon-based pricing. This study indicated that a higher total collected toll in link-based pricing is accompanied by a sharper reduction in congestion and pollution mitigation, which can be spent on alternative facilities and infrastructure by the municipality, such as the development of public transportation and parking.

Learning an Alternative Car-Following Technique to Avoid Congestion With an Instructional Driving Simulator

This article addresses the problem of traffic congestion through a learning perspective, highlighting the capabilities of information, and communication technologies to transform society. Recent physical and mathematical analysis of congestion reveals that training drivers to keep a safe distance systematically contributes to the emergence and maintenance of interference congestion (so-called phantom traffic jam). This article presents the WaveDriving Course (WDC), a simulated learning environment designed to help drivers progress from the traditional drive-to-keep-distance (DD) technique to a new car-following (CF) principle better suited for wave-like traffic, drive-to-keep-inertia (DI). The WDC is based on the ordinary knowledge of the driver (e.g., going through a series of traffic lights) and presents this situation in terms of two possible simultaneous behavioral strategies. The driver has the opportunity to verify that it is possible to achieve the same objective with different consequences. Finally, the WDC checks to what extent this learning generates transfer patterns in the analogous case of CF. This article focuses on results concerning the first WDC module: the traffic-light analogy. Forty-two participants followed the whole learning procedure for about 30 min. An evaluative CF test was administered before and after visioning the tutorial and practicing on the simulator. Overall, transference from this traffic-light analog to the CF situation (posttest) was successful. Results confirm the adoption of the expected DI strategies (speed variability decreased, distance and distance variability to leader increased, fuel consumption decreased, platoon elongation decreased, etc.). The need to improve the WDC teaching of the appropriate CF distance is discussed.

USERS’ EXPECTATION ON EAST COAST RAIL LINK (ECRL)

This paper focuses on the role of the new East Coast Rail Links (ECRL) project in Malaysia and how it will improve logistics efficiency, economic development, and environmental friendliness to meet users' expectations. The ECRL project aims to enhance connectivity between East and West Coast regions to stimulate industrial, commercial, and tourism development. The ECRL is a large-scale and heavily constructed railroad construction project. ECRL has not been a smooth project, with the government proposing the program in 2016 and construction beginning in 2017. However, due to various factors, it was suspended in 2018 and continued with a new rail route in 2019. Users' expectations for ECRL are mainly to reduce traffic congestion problems on the road, reduce high road transport costs, reduce pollution to the environment, drive business development and promote employment in the surrounding areas along the route. Because all these problems are becoming severe in Malaysia now, we will study ECRL and collect data from the respondents to point out the users' expectations of ECRL in terms of improving logistics efficiency, economic development, and environmental friendliness. After analysing the data, our study concludes with the result that all three variables positively influence users' expectations of ECRL, which, as a new railroad in Malaysia, will play a crucial role in the future development of the East Coast of Malaysia and Malaysia as a whole.

Investigating Urban Underground Space Suitability Evaluation Using Fuzzy C-Mean Clustering Algorithm—A Case Study of Huancui District, Weihai City

The development of underground space resources can alleviate the problems of traffic congestion and land resource tension caused by urbanization, but there are also certain risks in the development of underground space, so the suitability of development needs to be evaluated. This paper takes the geological suitability of underground space resources in Weihai City as the evaluation object, establishes the index system for evaluating the geological suitability of underground space resources development and utilization, determines the criteria for quantifying each factor index, uses the hierarchical analysis method to determine the index weights and applies the fuzzy C-mean clustering algorithm to evaluate the geological suitability of underground space resources development and utilization in the urban area of Weihai City, and achieves excellent results that are more in line with the geological conditions.

Hybrid deep learning models for traffic prediction in large-scale road networks

Traffic prediction is an important component in Intelligent Transportation Systems(ITSs) for enabling advanced transportation management and services to address worsening traffic congestion problems. The methodology for traffic prediction has evolved significantly over the past decades from simple statistical models to recent complex integration of different deep learning models. In this paper, we focus on evaluating recent hybrid deep learning models in the task of traffic prediction. To this end, we first conducted a review and taxonomize the reviewed models based on their feature extraction methods. We analyze their constituent modules and architectural designs. We select ten models representative of different architectural choices from our taxonomy and conducted a performance comparison study. For this, we reconstruct the selected models and performed a series of comparative experiments under identical conditions with three well-known real-world datasets collected from large-scale road networks. We discuss the findings and insights based on our results, highlighting the differences in the achieved prediction accuracy by models with different design decisions.

Research on highway traffic flow prediction model and decision-making method

In order to solve the problem of traffic congestion in a certain area, this paper develops a set of traffic optimization decision system. For analyzing the actual traffic conditions and calculating the traffic volume, density and traffic speed, a traffic prediction model is established and updated iteratively to modify the prediction model parameters. Based on this model, the congestion degree is estimated at the current road section, thus, an intelligent decision-making and the coordinated optimization methods are proposed. Moreover, this paper implements some application experiments on the isometric road of a three-intersection and obtains better prediction results of traffic density and traffic speed on the three-section highway. At the same time, compared with other existing prediction methods, the prediction model presented in this paper not only has higher accuracy, shorter prediction time and stronger anti-interference ability, but also has better effect on vehicle diversion. In addition, it also greatly relieves the traffic pressure on the road, maximizes the complementary advantages between intersections, and balances the good cooperation between each intersection.

Research on Consumer Identity in Using Sustainable Mobility as a Service System in a Commuting Scenario

Social and economic development leads to the continuous urbanization process, accompanied by the increasingly prominent traffic congestion problem. Traffic congestion also brings problems such as serious environmental pollution and intensified social conflicts. Therefore, how to alleviate traffic congestion has become a concern of the government and related researchers. As the main cause of urban traffic, the efficiency of urban commuting is considered as the best embodiment of urban functionality. The mobile as a service (Maas) system may be a better solution. In order to make the mobile as a service system more popular, it is necessary to understand the internal motivations driving people’s corresponding behaviors. Therefore, this study aimed to explore the internal logic of consumer identity, attitude and related intentions through a structural equation model and chi-square analyses. The final behavioral intention of commuting consumers towards MaaS was explored by integrating relevant theoretical models and comparing different levels of identification and the views of different consumers towards MaaS. A result was provided that can be used by relevant government agencies or practitioners in order to gain a deeper understanding of the internal perceptions of consumers and promote the optimization and innovation of MaaS systems. In addition, the results of this study also identified the potential necessary factors for the adoption of MaaS by commuting consumers, which may influence the improvement or development of relevant policies. The results showed that the model used in this study was applicable to the consumption cognition of the MaaS system in the commuting scenario, which shows that the model has a certain effect in explaining consumers’ sustainable consumption attitudes, identities and intentions. The internalization of sustainable identity is the most important part for consumers, which indicates that consumers attach great importance to self-value affirmation and mutual affirmation of sustainable concepts. However, in the consumer group, there were still some consumers who had not transformed their sustainable attitudes and recognitions into actual behavior. Converting such consumers is, therefore, crucial and an important step towards sustainable development.

Improved energy-efficient routing architecture for traffic management system using a hybrid meta-heuristic algorithm in Internet of vehicles

Today, Internet of Vehicles (IoV) applications communicate with smart devices and deploy Intelligent Transportation Systems (ITS) to recognize traffic congestion problems. One of the main challenges of smart cities and IoV platforms is data routing methods for smart traffic congestion problems to navigate the individual information of data transmission as a critical issue in the ITS. Providing a way to extend the Quality of Service (QoS) variables and energy efficiency methods for directing data transmission in routing-based traffic management systems within the IoV environment is an important issue since the energy consumption of IoV devices is a critical issue in low-power saving storage. This paper presents a hybrid Genetic Algorithm and Social Spider Optimization (GA-SSO) algorithm for an energy-aware routing schema for optimizing traffic congestion and smart devices in the IoV environment. After conducting some studies and comparisons, the exactness and prevalence of the proposed schema were received. Experimental results confirmed that the GA-SSO algorithm calculation decides optimal and ideal arrangement for energy-aware routing schema demonstrate by essentially making strides the execution of QoS variables can oversee information blockage comes from data transmission between IoV nodes.

Total Cost of Ownership of Light Commercial Electrical Vehicles in City Logistics

The process of urbanisation is one of the most characteristic features of the 20th century and the beginning of the 21st century. All economic and demographic forecasts indicate that the process of urbanisation will continue to develop dynamically. Continuous urbanisation generates a number of problems that are connected with issues such as urban freight transport, i.e., the problem of traffic congestion, noise and air pollution. Therefore, recent years have seen a dynamic growth in programmes intended to alleviate the negative impact of transport on the urban environment. A number of international projects have been implemented or initiated and resulted in the development of interesting solutions that enabled the rationalisation of transport and contributed to the development of sustainable urban logistics, e.g., BESTUFS, CITY PORTS, CityLog, CityMove, C-LIEGE, FREIGHTWISE, GRASS, NOVELOG, SMARTFREIGHT and SUGAR. Especially worthy of note amongst those initiatives are those which are concentrated on the implementation of BEVs (Battery Electric Vehicles). The authors of this paper have compared selected vehicles of the same brand and the same manufacturer, with the only difference being their N1 category power source (commercial vehicles with GVW of up to 3.5 tonnes), that are commonly used for the distribution of goods in urban conditions. The main purpose of the analysis was to answer the following question: can an electrical commercial vehicle compete in everyday use with a combustion-powered vehicle in the current market conditions? To this end, the authors developed a formula to calculate the total cost of ownership, in which all key criteria for vehicle use were taken into account, utilizing a scenario method. The utilitarian value of this research arises from the fact that Poland and its problems related to the issues analysed can serve as a source of preliminary analysis for other countries.

Public-Private Partnerships for Transit-Oriented Development: a case study on the potential of Hobart, Australia

Transit-Oriented Development (TOD) has been popular for urban development, especially for cities with transport network expansion. While TOD has been proved effective in facilitating mass transportation and making optimal use of property development, it is hardly a one-stop development strategy for solving issues such as traffic congestion. In this study, the city of Hobart is investigated with reference to the TOD metrics and experiences of public-private partnership (PPP). Hobart authorities have attempted to provide better living and working conditions while facing the problems of economic development and traffic congestion. The TOD metrics are proposed for application at different stages: (1) Travel Behaviour, (2) The Local Economy, (3) The Natural Environment, (4) The Built Environment, (5) The Social Environment, and (6) The Policy Context. Based on a literature review, a set of PPP metrics is proposed for the Hobart’s potential TOD framework. Drawn from on-site observations, key transport issues of Hobart are examined with the application of the PPP and TOD metrics. The results of this study could serve as a reference for regional cities in formulating their TOD strategies via PPP.

Urban Customized Bus Design for Private Car Commuters

With the deepening of the urbanization process, the ownership of urban private cars continues to increase, resulting in severe urban traffic congestion and environmental problems. The customized bus, as an emerging public transportation mode, is considered an effective means to alleviate the above problems. This article employs electronic registration identification (ERI) data of vehicles to design customized buses for private cars, consisting of two components: 1) discovering private car commuters and 2) designing customized bus schemes. First, based on the spatial–temporal similarity and high-frequency characteristics of commuting trips, we mined the urban private car commuters and their corresponding commuting trips as the demand for customized buses. Then, we constructed the customized bus model, which targets the number of served passengers with the constraints, such as the trip time window, bus capacity, passenger load rate, etc. In the model, intermediate stops are not set to ensure bus punctuality and passenger experience, and buses of various capacities are employed to ensure effectiveness and efficiency. The differential evolution algorithm was utilized to find the optimal solution for the model. In the experiments, we carried out relevant verification based on Chongqing’s one-week ERI data. The experimental results showed customized bus schemes for various cases and verified the superior performance of our algorithm by comparing it with general optimization algorithms. Besides, through numerical calculation and traffic simulation, the excellent potential for customized buses in reducing urban transportation energy consumption and urban road congestion is illustrated.

Vehicle Detection Monitoring System using Internet of Things

The overcapacity of vehicle numbers is one of the significant causes of the traffic congestion problem on Indonesia roadways. The government applies a One-way system (SSA) as one proposed solution to unravel the congestion. However, several congestion points are still found during the SSA implementation. Thus, this research offers an alternative method to detect congestion using IoT technology. The system automatically enumerates the number, classifies the type, and computes the speed averages of vehicles to identify the severity of congestion based on the Indonesian Highway Capacity Manual (IHCM) published by the Ministry of Public Works 2014. We utilize ultrasonic sensors to detect the vehicles and send the data to the server in real time. The research successfully develops an IoT system for traffic congestion detection. Communication between nodes and API can be done well. Data exchange involving encryption and decryption with AES-256 is successfully done. The website application developed in this research successfully show the severity level of the congestion and their vehicle numbers. The average accuracy of the system is 78,97%. The system detected more vehicles than actual numbers due to the misreading value of the sensors.

Real-time traffic congestion prediction using big data and machine learning techniques

PurposeThe study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.Design/methodology/approachThis study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.FindingsThe study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.Research limitations/implicationsThe model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.Practical implicationsTo help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.Social implicationsTo estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.Originality/valueThe proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.