Intelligent Public Transportation Research Articles

A Review of Multi-source Heterogeneous Twin Data Processing Methods in Traffic Scenes

As the complexity of intelligent public transportation systems continues to increase and the degree of interconnection of equipment at all levels gradually deepens, building a digital twin system will generate a large amount of multi-source heterogeneous data. Effective processing and in-depth mining of data can provide real-time and intelligent decision-making optimization for the construction of digital twin systems, thereby efficiently handling complex emergencies. This paper conducts a systematic review on the processing methods and technologies of multi-source heterogeneous twin data in traffic dynamic scenarios. First, the content and classification of multi-source heterogeneous twin data in the operation process of intelligent bus systems are clarified; secondly, the multi-source The data processing methods and technologies applied in each stage of data collection, data storage, data fusion and data analysis in heterogeneous twin data processing are analyzed, and the advantages, disadvantages and applications of various methods and technologies are analyzed; finally, corresponding to the multiple This paper summarizes the methods and technologies for processing multi-source heterogeneous twin data, and points out the challenges and development trends faced by the current multi-source heterogeneous twin data processing methods and technologies.

Time Series Prediction with Digital Twins in Public Transportation Systems

Classical traffic and transportation control centres are becoming insufficient with the rapid spread of electric, intelligent, autonomous, and software-defined vehicles. Existing traffic management strategies have significant drawbacks in public safety, predictive maintenance, tuning the core functionality of vehicles, and managing mobility. We can renovate this system with next-generation intelligent Digital Twin (DT) technologies. This research proposes a timeseries prediction system through Digital Twins to manage public transportation system with Facebook’s Prophet. This study presents a model framework to build Digital Twin application in Intelligent Public Transportation Systems and used a public data set to validate model with Facebook’s Prophet library by forecasting metro lines usage. Obtained results presented in the study and it is shown that forecasting error interms of Mean Absolute Percentage Error (MAPE) is 0.017 for 1 day horizon.

Performance evaluation of the cloud computing application for IoT-based public transport systems

The object of research is cloud computing as an element of the server infrastructure for intelligent public transport systems. Given the increasing complexity and requirements for modern transportation, the application of the Internet of Things concept has a high potential to improve efficiency and passenger comfort. Since the load generated in IoT systems is dynamic and difficult to predict, the use of traditional infrastructure with dedicated servers is suboptimal. This study considers the use of cloud computing as the main server infrastructure for the above systems. The paper investigates the main cloud platforms that can be used to develop such systems and evaluates their advantages and disadvantages. The authors developed the overall architecture of the system and evaluated the performance and scalability of individual components of the server infrastructure. To test the system, a software emulator was developed that simulates the controller module installed in vehicles. Using the developed emulator, stress tests were conducted to analyze and confirm the ability to scale and process input data by the proposed architecture. The test scenarios were developed and conducted on the basis of the existing public transportation system in Kyiv, Ukraine. The experimental results showed that the proposed IoT architecture is able to scale efficiently according to the load generated by the connected devices. It has been found that when the number of incoming messages increases from 40 to 6000, the average message processing time remains unchanged, and the error rate does not increase, which is an indicator of stable system operation. The obtained results can be used in the development of modern public transport systems, as well as for the modernization of existing ones

PAARGAMAN: Passenger Demand Provoked (On-The-Fly) Routing Of Intelligent Public Transport Vehicle with Dynamic Route Updation, Generation, and Suggestion

Demand-based public bus service meets the need of passengers with less money, time, and resources by reducing the number of private vehicles on the road. In contrast, dynamic real-time demand-based routing faces challenges like elevated travel time due to the requested assignment based on the paths and vehicle availability. Hence, this research introduces a novel framework named Passenger Influence Bus Service-Intelligent Public Transport System (PIBS-IPTS) for efficient routing of available vehicles based on the demand of passengers. For this, optimal paths are elected from the known routes of the general vehicle through the Cuckoo Search (CS) optimization algorithm. Then efficient route prediction is employed by the Artificial Neural Network (ANN) for passenger flow. Here, the unavailability of the passenger request, such as source location or Destination locations, or the unavailability of both locations is updated while employing the path generation process. The path generation process ensures the reduction of request drops generated by the passenger, which elevates the usage of the general bus service. Here, for the optimal selection of routes from the identified routing paths, a multi-objective function based on traffic density, route condition, and route mobility is employed for the selection of a near-optimal global solution. The method’s performance is analyzed using MAE, RMSE, and MAPE and obtained the best values of 0.69, 0.72, and 0.74, respectively.

Analysis of Data Coupling Mode of On-Board Information Acquisition and Communication

This paper studies the research status and development trend of the Internet of Vehicles and vehicle information acquisition system. According to the basic functional requirements and basic design principles of the vehicle information acquisition system, the overall design scheme of the system is proposed. The key technologies used in the system design are also studied Provide convenience for vehicle positioning and navigation services. In order to facilitate the intelligent dispatching of intelligent public transport vehicles, the information collection module designed by the system provides the collection of various information of vehicles, such as the number of people on board, the opening and closing of doors, fuel consumption and driving status. In this way, the control center can formulate corresponding more intelligent scheduling strategies, and manage vehicle operation more effectively and conveniently.

Reasoning over time into models with DataTime

Models at runtime have been initially investigated for adaptive systems. Models are used as a reflective layer of the current state of the system to support the implementation of a feedback loop. More recently, models at runtime have also been identified as key for supporting the development of full-fledged digital twins. However, this use of models at runtime raises new challenges, such as the ability to seamlessly interact with the past, present, and future states of the system. In this paper, we propose a framework called DataTime to implement models at runtime which capture the state of the system according to the dimensions of both time and space, here modeled as a directed graph where both nodes and edges bear local states (i.e., values of properties of interest). DataTime offers a unifying interface to query the past, present, and future (predicted) states of the system. This unifying interface provides (i) an optimized structure of the time series that capture the past states of the system, possibly evolving over time, (ii) the ability to get the last available value provided by the system’s sensors, and (iii) a continuous micro-learning over graph edges of a predictive model to make it possible to query future states, either locally or more globally, thanks to a composition law. The framework has been developed and evaluated in the context of the Intelligent Public Transportation Systems of the city of Rennes (France). This experimentation has demonstrated how DataTime can be used for managing data from the past, the present, and the future and facilitate the development of digital twins.

Optimization design of real-time scheduling scheme for city bus vehicles based on BP neural network

Urban public transportation is inseparably related to people’s travel and life. Prioritizing the development of public transportation is a major national policy proposed by the Chinese government, especially to actively promote the development of intelligent public transportation systems. Developing and building advanced intelligent public transport operation and scheduling management system, designing efficient, flexible and low-cost operation and scheduling mode, improving the management level and service quality of public transport enterprises, and thus improving the road traffic condition of the whole city, is undoubtedly the future development direction of public transport enterprises. This paper combines the problems of bus operation and scheduling in China at the present stage, and proposes the operation and scheduling model and method of the intelligent urban public transportation planning system, with a view to realizing a flexible public transportation operation model suitable for different service situations and providing systematic theoretical support for solving the imbalance between supply and demand of public transportation.

Improving the Prediction of Passenger Numbers in Public Transit Networks by Combining Short-Term Forecasts With Real-Time Occupancy Data

Passengers of public transportation nowadays expect reliable and accurate travel information. The need for occupancy information is becoming more prevalent in intelligent public transport systems as people started avoiding overcrowded vehicles during the COVID-19 pandemic. Furthermore, public transportation companies require accurate occupancy forecasts to improve service quality. We present a novel approach to improve the prediction of passenger numbers that enhances a day-ahead prediction with real-time data. We first train a baseline predictor on historical automatic passenger counting data. Next, we train a realtime model on the deviations between baseline prediction and observed values, thus capturing events not addressed by the baseline. For the forecast, we attempt to detect emerging patterns in real time and adjust the baseline prediction with deviations from the patterns. Our experiments with data from Germany show that the proposed model improves the forecast of the baseline model and is only outperformed by artificial neural networks in some instances. If the training sets only cover a limited period of up to four months, our approach outperforms competing methods. For larger training sets, there are mixed results in the sense that for some test cases, certain types of neural networks yield slightly better results, but our method still performs well with less training effort, is explainable along the whole prediction process and can be applied to existing prediction methods.

Multiple Extreme Learning Machines Based Arrival Time Prediction for Public Bus Transport

Due to fast-growing urbanization, the traffic management system becomes a crucial problem owing to the rapid growth in the number of vehicles The research proposes an Intelligent public transportation system where information regarding all the buses connecting in a city will be gathered, processed and accurate bus arrival time prediction will be presented to the user. Various linear and time-varying parameters such as distance, waiting time at stops, red signal duration at a traffic signal, traffic density, turning density, rush hours, weather conditions, number of passengers on the bus, type of day, road type, average vehicle speed limit, current vehicle speed affecting traffic are used for the analysis. The proposed model exploits the feasibility and applicability of ELM in the travel time forecasting area. Multiple ELMs (MELM) for explicitly training dynamic, road and trajectory information are used in the proposed approach. A large-scale dataset (historical data) obtained from Kerala State Road Transport Corporation is used for training. Simulations are carried out by using MATLAB R2021a. The experiments revealed that the efficiency of MELM is independent of the time of day and day of the week. It can manage huge volumes of data with less human intervention at greater learning speeds. It is found MELM yields prediction with accuracy in the range of 96.7% to 99.08%. The MAE value is between 0.28 to 1.74 minutes with the proposed approach. The study revealed that there could be regularity in bus usage and daily bus rides are predictable with a better degree of accuracy. The research has proved that MELM is superior for arrival time predictions in terms of accuracy and error, compared with other approaches.

Du-Bus: A Realtime Bus Waiting Time Estimation System Based On Multi-Source Data

Realtime bus waiting time information is of great importance to the intelligent public transportation system and is beneficial for improving user satisfaction by online map services. While there are limited realtime bus waiting time services in a city, because of the expensive cost of sensor deployment and sophisticated traffic conditions. To address the above problem, we propose Du-Bus, a multi-source data fusion based system, which estimates the realtime bus waiting time based on approximating the realtime locations of buses without GPS sensors, by a variety of urban datasets, including historical bus trip data reported by a limited number of GPS equipped buses, transportation network data, traffic condition data, user mobility data, and temporal data. Du-Bus approximates the realtime locations of buses without GPS sensors by jointly modeling the bus timetable and the bus realtime travel time, which can be estimated by a variety of data sources. Specifically, we first propose a BiLSTM based end-to-end model for each bus route to estimate the bus departure interval and generate the corresponding departure timetable. Then, we estimate the travel time for each individual bus via a deep neural network component by incorporating the traffic conditions, geolocation, and map query information. Finally, we estimate the bus waiting time for arbitrary stations in the city by jointly modeling the estimated bus departure timetable and travel time. We evaluate our system on two real-world datasets, and the results verify the effectiveness of Du-Bus compared with historical average based and headway based methods. Since early 2019, Du-Bus has been deployed on Baidu Maps, one of the world’s largest map services, servicing over 20 major cities in China.

Analysis and Visulization of Public Transport for Integrated monitoring dashboard : Case Study of Thane, Maharashtra, India

Urban population of India and migration towards large city has been increased in last decade. Correspondingly civic management has to deliver various facilities on timeline based for several services. Smart City mission launched by Government of India focuses on serval attributes where Intelligent Public Transport System is major focus. Currently 68 major cities in India have public transport system in city. 83 of the 100 shortlist smart cities have implemented an Integrated Command and Control Center (ICCC) to gather, analyze, evaluate, and respond to big data inputs from across the urban area. We have reported various critical attributes for visualization and analytics of Intelligent Public Transport system. Real database of Thane Smart have been taken for academic research purpose. Various analysis reports have been prepared and correspondingly visualization for dashboard have been successfully created. All reports contain interactive details with attributes including 3D trip layers. Common data tables designed for various analysis have also been suggested. It is recommended to have common features for critical attributes for Intelligent Transport System (ITS) for all smart cities for better common implementation policy including fleet size, and shifting to Electric Vehicles (EV).

Intelligent Bus Operation Optimization by Integrating Cases and Data Driven Based on Business Chain and Enhanced Quantum Genetic Algorithm

Intelligent public transport systems are a key direction for the study of intelligent transportation systems (ITSs), and they perform the following functions: location and tracking, aided navigation, dispatch and command, and dynamic information release; these systems also help travelers determine optimal routes. This paper mainly studies intelligent bus operation optimization by integrating cases and data from business chains, including the optimization of public transport vehicle scheduling according to the characteristics of vehicle scheduling, considers the interests of passengers and public transport companies, and adopts a true-value encoding method with the departure time as the variable goal optimization. In addition, this paper builds a model for the intelligent scheduling problem in public transport based on an enhanced quantum genetic algorithm (EQGA) to find the optimal timetable. This model sets the minimum waiting time cost of passengers and the maximum interests of public transport companies as the goals, restricts the departure interval and two adjacent intervals, and constrains the load factor of passengers. Moreover, based on the analysis of public transport travel characteristics and passenger flow data, this paper evaluates the efficiency of public transport operation, reasonably analyzes the utilization of public transport resources and the travel time of public transport passengers, and thoroughly studies the public transport operation system. This paper selects actual bus line data for empirical analysis, and the empirical results show that the proposed algorithm and model can meet the requirements of many aspects and provides good intelligence, applicability, and optimization.

A2E2: Aerial-assisted energy-efficient edge sensing in intelligent public transportation systems

Emerging intelligent public transportation systems (IPTS) enable various smart services by collecting sensing data leveraging public transportation. However, existing researches ignore sensors are usually deployed in 3-D space, and a number of generated sensing data are dropped due to limited storage capacities of sensors. To solve the problem, we propose the A2E2 framework to ensure the sustainable sensing operation, i.e., collecting data and charging sensors assisted by UAVs in IPTS simultaneously. In A2E2 framework, UAVs could charge sensors and fly near them to collect sensing data. Meanwhile, they also could stop on vehicles to be charged and send collected data to smart services. The entire process is formulated as a collaborative task allocation and scheduling jointly optimization problem. We solve the problem by dividing it into two parts which are proved NP-hard, and propose two algorithms, i.e., the minimum transfer algorithm and shortest trajectory planning algorithm. The minimum transfer algorithm minimizes the number of UAV transfer times among vehicles, and the shortest trajectory planning algorithm ensures UAVs could execute the allocated task effectively. Extensive evaluations with large scale real world dataset are conducted showing that A2E2 outperforms the other three benchmarks significantly.

Smart public transportation: A future framework for sustainable new cities (Case study Greater Cairo)

In the context of the tremendous progress in information technology in all spheres of life, it is necessary to adopt more intelligence solutions to increase the quality of life and achieve sustainable cities. Currently, without a doubt, we have the ability to make all city services accessible to everybody. Transportation and mobility systems are critical components of sustainable city development. Therefore, the scientific literature on the technological systems employed in transportation systems and their primary uses is extensive, outlining their application to improving people’s quality of life through promoting a sustainable environment. Recently. There is a new generation of ITS (intelligence transportation system) resulting from the encouragement of related public policy that has been deeply integrated with new infrastructure and new technologies for improving the quality of life. The new generation of ITS technology and urban analysis and planning technology can not only meet the current problems of the coordinated development of the public transportation system and big cities like Cairo, but also meet the needs of the city’s future development in advance. This paper will discuss the transformation of intelligent public transportation, the opportunities, and applications, using technologies, and implementation in various urban areas using a case study of China’s smart transportation. Additionally, propose a future framework for sustainable transportation in new cities and the feasibility of implementing them in Egypt’s new cities. Highlight the actions and outcomes of integrating technologies, as well as their environmental, urban, economic, and social impacts. In order to develop and increase the quality of life in the cities of Egypt and the developing world in order to achieve sustainability goals.

Towards intelligent public transport systems in Smart Cities; Collaborative decisions to be made

The global context of cooperative intelligent public transport systems (C-ITS) in Smart Cities calls for a solution that integrates the perspectives of the travelers, public administration, vehicle manufacturers and transport operators in the joint effort of standardization, interconnection and assimilation of emerging technologies to meet operational, safety and environmental objectives at metropolitan, national or international level. The article proposes a definition for the capabilities maturity levels of the mobility ecosystem, and functional architecture for the collaborative decision-making system for implementing the C-ITS in the future Smart Cities. The paper contributes to the literature and practice in four manners. The first one is enhancing the awareness and understanding of maturity capability model (CMM) for all the stakeholders throughout the mobility ecosystem, emphasizing the key capabilities required for each maturity level. The second way is manifested throughout public and private companies acting on the same territory in the mobility ecosystem assess the existing capabilities and collaborate for achieving a superior maturity level of mobility processes, stable and repeatable results. In addition, the third one, based on CMM, is that governments regulate, conduct and control, establishing a stable and mature processes institutionalization for C-ITS. Finally, the fourth practice is defining a C-ITS functional architecture for the future Smart Cities to support collaborative decision-making applied for public transport implementation in big Smart Cities with the final objective of increasing the citizen’s quality of life.

Smart Bus System for Specially Challenged Individuals

This paper introduces a system design about bus management system based on Wi-Fi module which implemented the basic functions of the intelligent public transport management system, such as monitoring the time of bus arrival, departing from the bus station and reporting stations name automatically. This system can ensure the punctuality of vehicles to run, improve the automation level of reporting stations, and quality of public transport service. The management system has low cost and thus it is more feasible. This System can be a brilliant helping hand for the individuals who are mentally and physically challenged and help them in reaching their respective destinations on time because, in our respective transportation system, the priority will be to drop the physically and mentally impaired individuals as well as senior citizens safely on time.

Intelligent public transport cloud platform dispatching operation management system development

In recent years, with the rapid development of various cities, the operation and maintenance of the public transport system has become increasingly prominent problems, the development of the intelligent public transport system has been imperative. This paper takes the intelligent public transport system as the research object, using wireless network transmission technology, big data retrieval and processing, cloud computing platform and other technologies, analyzes the main requirements of the intelligent public transport system. This system adopts the classic three-layer architecture of the Internet of Things, namely, the perception layer, the network layer and the platform layer. Intelligent public transport dispatching system is the core system of intelligent public transport. The paper uses frog leaping algorithm as the dispatching algorithm to realize the function of public transport dispatching. Through the test, it can complete the task of adding or reducing vehicles according to the increase or decrease of passenger flow. The design and implementation of the cloud platform provides a strong support for the renewal and development of modern urban public transportation system, and has a certain guiding significance. As a complete system with more rapid and accurate management, more convenient and efficient operation, it improves the public transport dispatching ability and improves the travel experience of passengers, and has a cross-era significance for the development of urban public transportation.

Research and Simple Calculations on Public Transport Card Data

Big data is one of the hottest topics nowadays. Under the background of smart city and intelligent public transport, a large number of scholars begin to use big data of public transport in urban transport planning. This paper, combined with dozens of the latest journal papers, combined with their own understanding, focuses on the development of bus IC card big data, application status and research prospects, forming a reading report.

Driving Behavior Analysis of City Buses Based on Real-Time GNSS Traces and Road Information.

The driving behavior of bus drivers is related to the safety of all passengers and regulation of urban traffic. In order to analyze the relevant characteristics of speed and acceleration, accurate bus trajectories and patterns are essential for driver behavior analysis and development of effective intelligent public transportation. Exploiting real-time vehicle tracking, this paper develops a platform with vehicle-mounted terminals using differential global navigation satellite system (DGNSS) modules for driver behavior analysis. The DGNSS traces were used to derive the vehicle trajectories, which were then linked to road information to produce speed and acceleration matrices. Comprehensive field tests were undertaken on multiple bus routes in urban environments. The spatiotemporal results indicate that the platform can automatically and accurately extract the driving behavior characteristics. Furthermore, the platform’s visual function can be used to effectively monitor driving risks, such as speeding and fierce acceleration, in multiple bus routes. The details of the platform’s features are provided for intelligent transport system (ITS) design and applications.

Privacy and Security Management in Intelligent Transportation System

Metropolitan transportation is a dynamic and non-linear complex system. In such a system, there are possibilities of altering, monitoring, forging, and accessing private, public, and resource information of depot staff and communicating agents by unauthorized agencies the metropolitan area. Existing solutions for the management of security and privacy of communicating agents in an intelligent public transportation system (IPTS) do not adapt to the dynamic occurrence of real-time event information. Therefore, existing solutions are insufficient to address the randomness and other characteristics pertaining to a non-linear complex system such as an intelligent transport system (ITS). To this end, in this article, we propose a privacy and security management scheme for ITS depot staff in a metropolitan area. This scheme provides privacy and security management in the transportation industry during the exchange of information regarding vehicle allocation, dispatch, revocation, financial, and maintenance. Absence of such an aforementioned scheme leads to anomalies such as impersonation of genuine staff and malicious and greedy staff. We use the emergent intelligence (EI) technique to collect, analyze, and share information, and take dynamic decisions during the security and privacy management of the depot staff in transport industries. The EI technique provides autonomy, flexibility, adaptiveness, robustness, self-organization, and evolution to address the randomness and behavior of a non-linear complex system pertaining to the transportation system in metropolitan areas. The proposed scheme is implemented using the Crypto++ package, and the results indicate that the scheme efficiently manages the security and privacy in transportation industries in metropolitan areas.