Real-Time and Predictive Analytics for Smart Public Transportation Decision Support System

Sun, Fangzhou; Pan, Yao; White, Jules; Dubey, Abhishek

doi:10.1109/smartcomp.2016.7501714

Cited by 33 publications

(12 citation statements)

References 22 publications

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“…Of these, Wangyang et al [215] and Xiao et al [216] use deep learning based sequential modeling approaches to predict traffic flow ahead of time where as Aung & Naing [213] and Yunxiang Liu & Wu [214] solve this through a classification formulation. A traffic management system for public buses has been proposed in [32,217] where GPS data is used to predict bus arrival times for public transportation systems. Accident detection has been performed using car position and velocity information in a VANET environment by the authors of [218] in a cloud based system that can use this information to predict whether an accident has occurred or not.…”

Section: Smart Transportmentioning

confidence: 99%

“…A summary of IoT based AI for Smart Transport has been given in Table 12. [207] LR [206] Homogeneous (RFID data from cars) DNN+ CNN [208] Classification-Different positions based on beacons installed Homogeneous (Radio frequency signal strength) DT [209] Classification [217] Regression-Arrival time prediction RF [220] Classification-Localization, as on platform or train Homogeneous (Wi-Fi signal parameters) Transport management (Traffic flow) NB [213] Cloud Classification-Different traffic states Homogeneous (GPS data, current and historical) RF [214] Heterogeneous (Weather, Road data) RNN (LSTM) [216] Regression-Traffic flow prediction Homogeneous (Traffic flow data [vehicle speed count etc.]) RNN (LSTM) [221] SAE + RNN (LSTM) [215] Transport management (Traffic Accident detection) RF [218] Classification-Accident or not Homogeneous (Velocity, Position)…”

Section: Smart Transportmentioning

confidence: 99%

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IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

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Section: Smart Transportmentioning

confidence: 99%

Section: Smart Transportmentioning

confidence: 99%

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

et al. 2021

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“…Using AVL systems as a data source, Sun [ 19 ] proposed a system to predict the arrival time of buses at stops by combining clustering techniques with other methods. Based on positioning data and, more specifically, on data generated by GPS devices on taxis, some studies have generated mobility patterns in urban areas.…”

Section: Related Studiesmentioning

confidence: 99%

Applying Time-Dependent Attributes to Represent Demand in Road Mass Transit Systems

Cristóbal

Padrón

Lorenzo-Navarro

et al. 2018

Entropy

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Abstract:The development of efficient mass transit systems that provide quality of service is a major challenge for modern societies. To meet this challenge, it is essential to understand user demand. This article proposes using new time-dependent attributes to represent demand, attributes that differ from those that have traditionally been used in the design and planning of this type of transit system. Data mining was used to obtain these new attributes; they were created using clustering techniques, and their quality evaluated with the Shannon entropy function and with neural networks. The methodology was implemented on an intercity public transport company and the results demonstrate that the attributes obtained offer a more precise understanding of demand and enable predictions to be made with acceptable precision.

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“…Due to this discouraging situation, the number of cars and motorcycles on the road cannot be decreased efficiently and the goal of introducing an efficient public transportation system is not achieved. Thus, in order to remedy the situation, the improvement of the public bus system is a critical issue [2]. A few bus stops in Taichung City introduced LED screens which show predicted arrival times of the buses.…”

Section: Introductionmentioning

confidence: 99%

Arrival Time Estimation and Reminders on Public Bus System

2017

Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering

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The public bus service, which is one of the most frequently used public transportation methods, is an indispensable part of public lifestyle. Hence, government and companies have integrated the public bus service with smart device applications, enabling the public to check information such as bus schedules and instant information at any time. Their aim is to improve the efficiency of the public bus service. Unfortunately, their efforts have faced a variety of problems, such as the distrust of the public regarding the provided information, traffic situation, and unexpected incidents which disturb the bus schedule. In order to overcome the mentioned problems, a smart bus stop signboard system will be designed. The smart signboard will show the current position of the bus and provides a reservation service, which the public may access using smart devices. The proposed system allows bus drivers to obtain information about a particular bus stop to determine whether they should drive to that bus stop. Because the traffic situation and the embarkment and disembarkment of passengers affects the arrival time of the bus, the proposed system will take the change in the number of bus passengers and travel time into account in order to predict the arrival time of the bus.

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Real-Time and Predictive Analytics for Smart Public Transportation Decision Support System

Cited by 33 publications

References 22 publications

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

Applying Time-Dependent Attributes to Represent Demand in Road Mass Transit Systems

Arrival Time Estimation and Reminders on Public Bus System

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