Smart Parking-Management System for Commercial Vehicle Parking at Public Rest Areas

Bayraktar, Mehmet Emre; Arif, Farrukh; Özen, Halit; Tuxen, Gorm

doi:10.1061/(asce)te.1943-5436.0000756

Cited by 23 publications

(16 citation statements)

References 4 publications

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“…The Kalman filter is an efficient recursive computational method to predict or estimate the state of a process. It is based on a set of mathematical equations that try to minimize the mean squared error and it works on a so-called prediction-correction approach [23]. In the first step of the Kalman Filtering Analysis, Eq.…”

Section: Methodsmentioning

confidence: 99%

“…The prediction equations are responsible for projecting forward the current state and error covariance estimates to obtain the a priori estimates for the next step. The correction equations are used for integrating new measurements into a previous estimate to obtain an improved a posteriori estimate [23]. For reliability, short time incidents of less than five minutes that did not have an effect on traffic flow and incident data that had uncertain characteristics were excluded from the data set.…”

Section: Methodsmentioning

confidence: 99%

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Multi-Step Approach to Improving Accuracy of Incident Duration Estimation: Case Study of Istanbul

Özen

Saraçoğlu

2019

Teh. vjesn.

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Unexpected events, such as crashes, disabled vehicles and spilled loads cause traffic congestion or increase the level of existing congestion on roads. Traffic incident management requires the estimation of incident duration from the beginning to the final stage of the event, in order to identify the most suitable strategies for decreasing their impact. Accordingly, the adverse effects of an incident can be minimised by efficiently managing the process between the time the incident occurred and the traffic returning to normal operating conditions. The purpose of this paper is to present a methodology and improve the accuracy of the model by using a multi-step approach to estimate the duration of an incident. For this purpose, incident duration data was obtained for Istanbul Transit Europe Motorways (TEM). Using the incident data and implementing the multi-step approach, Kalman Filtering and linear regression analysis in stepwise, were used for the incident duration estimation model.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Multi-Step Approach to Improving Accuracy of Incident Duration Estimation: Case Study of Istanbul

Özen

Saraçoğlu

2019

Teh. vjesn.

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“…There is also no feedback loop with actual occupancy measurements and therefore no possibility to account for irregular events. Bayraktar et al (2014) propose a Kalman Filter approach. The work focuses not only on truck parking occupancy prediction but also on a "Smart-Parking Management System".…”

Section: Urban Car Parking Predictionmentioning

confidence: 99%

“…Automatic truck counting technology is the basis of Intelligent Truck Parking (ITP), which "is concerned with the management of information related to Truck Parking Area (TPA) operations for HGV" (Sochor and Mbiydzenyuy, 2013). The benefits of ITP, according to Bayraktar et al (2014), are increased operational efficiency of the drivers, reduced parking on shoulders, reduced fatigue-related crashes, and the reduction of diesel emissions.…”

Section: Research Gapmentioning

confidence: 99%

“…The later a search is started, the less likely a suitable parking spot will be found. This is due to a general lack of truck parking spots along motorways (Garber et al, 2004;Sun et al, 2018;Nevland et al, 2020), which is predicted to become even worse because of increased commercial traffic (Bayraktar et al, 2014). The situation is illustrated by the example of the United States but also applies to other countries.…”

Section: Introductionmentioning

confidence: 99%

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Truck Parking Occupancy Prediction: XGBoost-LSTM Model Fusion

Gutmann

Maget

Spangler

et al. 2021

Front. Future Transp.

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For haul truck drivers it is becoming increasingly difficult to find appropriate parking at the end of a shift. Proper, legal, and safe overnight parking spots are crucial for truck drivers in order for them to be able to comply with Hours of Service regulation, reduce fatigue, and improve road safety. The lack of parking spaces affects the backbone of the economy because 70% of all United States domestic freight shipments (in terms of value) are transported by trucks. Many research projects provide real-time truck parking occupancy information at a given stop. However, truck drivers ultimately need to know whether parking spots will be available at a downstream stop at their expected arrival time. We propose a machine-learning-based model that is capable of accurately predicting occupancy 30, 60, 90, and 120 min ahead. The model is based on the fusion of Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) with the help of a feed-forward neural network. Our results show that prediction of truck parking occupancy can be achieved with small errors. Root mean square error metrics are 2.1, 2.9, 3.5, and 4.1 trucks for the four different horizons, respectively. The unique feature of our proposed model is that it requires only historic occupancy data. Thus, any truck occupancy detection system could also provide forecasts by implementing our model.

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Toward a real‐time Smart Parking Data Management and Prediction (SPDMP) system by attributes representation learning

Yang

Cui

et al. 2021

Int J of Intelligent Sys

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Managing and estimating the availability information of parking lots is of great importance to travelers and managers. However, the task is very challenging since the occupancy rate is affected by various factors, including spatial-temporal features, parking lot attributes features, and environmental changes. Previous studies mostly focus on the short-term prediction by capturing the historical sequential dependencies among inputs and outputs, which leads to low estimation accuracy for long-term prediction and limited scalability for real deployment in parking lots. To address the challenges, a comprehensive framework for real-time Smart Parking Data Management and Prediction (SPDMP) system is proposed. Three types of data sources, including historical sequential data, real-time sequential data, and attributes category data, are sufficiently integrated into a customized Parking Availability Prediction (PAP) neural network by representation learning and heterogeneous feature embedding. Specifically, instead of using parking lot property information and environmental data directly, the authors design an Attributes Tensor Embedding Component (ATEC) to integrate the intra-class affection and interclass representations and correlations by two steps of customized embedding process. To balance the impact of the indiscriminate features for various

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Smart Parking-Management System for Commercial Vehicle Parking at Public Rest Areas

Cited by 23 publications

References 4 publications

Multi-Step Approach to Improving Accuracy of Incident Duration Estimation: Case Study of Istanbul

Multi-Step Approach to Improving Accuracy of Incident Duration Estimation: Case Study of Istanbul

Truck Parking Occupancy Prediction: XGBoost-LSTM Model Fusion

Toward a real‐time Smart Parking Data Management and Prediction (SPDMP) system by attributes representation learning

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