Contributions of Transportation Network Modeling to the Development of a Real-Time Route Guidance System

Providing reliable real-time travel time information is a critical challenge to all existing traffic routing systems. This study develops a new model for estimating and predicting real-time traffic conditions and travel times for variable message signs-based route guidance system. The proposed model is based on real-time limited detected traffic data, stochastic nonlinear macroscopic traffic flow model, and adaptive Kalman filtering theory. The method has the following main features: (1) real-time estimation and prediction of traffic conditions on a network level using limited traffic detectors, (2) travel time prediction in free flow and congested flow, and (3) prediction of drivers’ en-route diversion behavior. Field testing is conducted based on the Route Guidance Pilot Project sponsored by the National Science and Technology Ministry of China. The achieved testing results are satisfactory and have potential use for future works and field applications.

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ReFOCUS+: Multi-Layers Real-Time Intelligent Route Guidance System With Congestion Detection and Avoidance

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2019.2952524 ◽

2019 ◽

pp. 1-14 ◽

Cited By ~ 2

Author(s):

Mahboobe Rezaei ◽

Hamed Noori ◽

Mohsen Mohammadkhani Razlighi ◽

Mohsen Nickray

Keyword(s):

Real Time ◽

Guidance System ◽

Route Guidance ◽

Congestion Detection

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Evaluating Effectiveness of Real-Time Advanced Traveler Information Systems Using a Small Test Vehicle Fleet

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1588-06 ◽

1997 ◽

Vol 1588 (1) ◽

pp. 41-48 ◽

Cited By ~ 9

Author(s):

Christopher L. Saricks ◽

Joseph L. Schofer ◽

Siim Sööt ◽

Paul A. Belella

Keyword(s):

Travel Time ◽

Real Time ◽

Time Of Day ◽

Guidance System ◽

Effective Characteristics ◽

Local Network ◽

Route Guidance ◽

Time Data ◽

Traveler Information ◽

Link Travel Time

ADVANCE was an in-vehicle advanced traveler information system (ATIS) providing route guidance in real time that operated in the northwestern portion and northwest suburbs of Chicago, Illinois. It used probe vehicles to generate dynamically travel time information about expressways, arterials, and local streets. Tests to evaluate the subsystems of ADVANCE, executed with limited availability of test vehicles and stringent scheduling, are described; they provided useful insights into both the performance of the ADVANCE system as a whole and the desirable and effective characteristics of ATIS deployments generally. Tests found that the user features of an in-route guidance system must be able to accommodate a broad range of technological sophistication and network knowledge among the population likely to become regular users of such a system. For users who know the local network configuration, only a system giving reliable real-time data about nonrecurrent congestion is likely to find a market base beyond specialized applications. In general, the quality and usefulness of systemwide real-time route guidance provided by other means are enhanced significantly by even a small deployment of probes: probe data greatly improve static (archival average) link travel time estimates by time of day, although the guidance algorithms that use these data should also include arterial traffic signal timings. Moreover, probe- and detector-based incident detection on arterial networks shows considerable promise for improved performance and reliability.

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Integrated mode of transport: a predictive model for route guidance

International Journal of Intelligent Unmanned Systems ◽

10.1108/ijius-09-2020-0040 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Roopa Ravish ◽

Shanta Rangaswamy

Keyword(s):

Predictive Model ◽

Real Time ◽

Road Network ◽

Guidance System ◽

Intelligent Transport System ◽

Route Guidance ◽

Alternative Route ◽

Content Type ◽

Optimal Mode ◽

Mode Of Transport

PurposeThe purpose of this study is to provide real-time route guidance within city to help commuters.Design/methodology/approachIn urban areas to avoid road congestion and to reach the destination on time, intelligent transport system (ITS) utilizes recent advanced technology. To support this, existing route guidance system (RGS) suggests alternative route to commuters. However, ITS requires a system which suggests the alternative route along with the mode of transport such as public, private, taxi services etc. Integrated mode of transport (IMT) implemented in this paper guides the commuters of urban area with the best mode of transport. Inputs to our IMT predictive model are the commuter's choice of (1) minimum travel time (2) minimum cost (3) flexible route and (4) less traffic intensity along with source and destination locations. Based on these user inputs, IMT predictive model suggests optimal mode of transport. In this paper to implement the IMT model, we have considered the transport facility available in Bangalore, a city in India. The city has metro train, bus and taxi services available to the commuters. Implementation is divided into two parts. In the first part, the model checks for the end-to-end connectivity/availability of metro train facility. If metro train connectivity exists, the model concludes this as the best mode of travel. In the second part, for the routes which are not connected by metro train, the optimal mode of transport through road network will be suggested. In the first part, to check the existence of metro train along the routes between source and destination, location-IQ API is used. In the later part, to suggest transport along the road network, Q-learning algorithm of reinforcement learning technique is used.FindingsThe findings are the predictive model algorithm to find the best mode of transfer and reinforcement model used in real time route guidance system.Originality/valueThis is a new Idea, not proposed in any research work.

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An Eco-Friendly Multimodal Route Guidance System for Urban Areas Using Multi-Agent Technology

Applied Sciences ◽

10.3390/app11052057 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2057

Author(s):

Abdallah Namoun ◽

Ali Tufail ◽

Nikolay Mehandjiev ◽

Ahmed Alrehaili ◽

Javad Akhlaghinia ◽

...

Keyword(s):

Traffic Flow ◽

Urban Areas ◽

Urban Environments ◽

Ease Of Use ◽

Transport Infrastructure ◽

Guidance System ◽

Planning System ◽

Flow Density ◽

Route Guidance ◽

Agent Based

The use and coordination of multiple modes of travel efficiently, although beneficial, remains an overarching challenge for urban cities. This paper implements a distributed architecture of an eco-friendly transport guidance system by employing the agent-based paradigm. The paradigm uses software agents to model and represent the complex transport infrastructure of urban environments, including roads, buses, trolleybuses, metros, trams, bicycles, and walking. The system exploits live traffic data (e.g., traffic flow, density, and CO2 emissions) collected from multiple data sources (e.g., road sensors and SCOOT) to provide multimodal route recommendations for travelers through a dedicated application. Moreover, the proposed system empowers the transport management authorities to monitor the traffic flow and conditions of a city in real-time through a dedicated web visualization. We exhibit the advantages of using different types of agents to represent the versatile nature of transport networks and realize the concept of smart transportation. Commuters are supplied with multimodal routes that endeavor to reduce travel times and transport carbon footprint. A technical simulation was executed using various parameters to demonstrate the scalability of our multimodal traffic management architecture. Subsequently, two real user trials were carried out in Nottingham (United Kingdom) and Sofia (Bulgaria) to show the practicality and ease of use of our multimodal travel information system in providing eco-friendly route guidance. Our validation results demonstrate the effectiveness of personalized multimodal route guidance in inducing a positive travel behavior change and the ability of the agent-based route planning system to scale to satisfy the requirements of traffic infrastructure in diverse urban environments.

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