OD-Matrix Extraction based on Trajectory Reconstruction from Mobile Data

Pourmoradnasseri, Mozhgan; Khoshkhah, Kaveh; Lind, Artjom; Hadachi, Amnir

doi:10.1109/wimob.2019.8923358

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…Let N = (η m ) be a normalized matrix with ∑ m η m = 1, such that each entry η m suggests an estimated ratio of trips between mth OD pair to all trips. Normalized trip distribution can be obtained from different data sources, such as travel surveys [35], census of population and housing [21], mobile data [36,37], public transport smart tickets [38,39], or geo-tagged social media posts [40]. We refer to this input as a normalized OD (NOD) matrix.…”

Section: Initial Nod Matrixmentioning

confidence: 99%

Leveraging IoT data stream for near‐real‐time calibration of city‐scale microscopic traffic simulation

Pourmoradnasseri,

Khoshkhah,

Hadachi

2023

IET Smart Cities

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The emergence of smart cities is set to transform transportation systems by leveraging real‐time traffic data streams to monitor urban dynamics. This complements traditional microscopic simulation methods, offering a detailed digital portrayal of real‐time traffic conditions. A framework for near‐real‐time city‐scale traffic demand estimation and calibration is proposed. By utilising Internet of Things (IoT) sensors on select roads, the framework generates microscopic simulations in congested networks. The proposed calibration method builds upon the standard bi‐level optimization formulation. It presents a significant computational advantage over available methods by (i) formulating the optimization problem as a bounded variable quadratic programming, (ii) acquiring sequential optimization technique by splitting computations into short time frames while considering the dependency of the demand in successive time frames, (iii) performing parallel simulations for dynamic traffic assignment in corresponding time frames using the open source tool Simulation of Urban MObility (SUMO), and (iv) feeding traffic count data of each time frame as a stream to the model. The approach accommodates high‐dimensional real‐time applications without extensive prior traffic demand knowledge. Validation in synthetic networks and Tartu City case study showcases scalability, accuracy, and computational efficiency.

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Section: Initial Nod Matrixmentioning

confidence: 99%

Leveraging IoT data stream for near‐real‐time calibration of city‐scale microscopic traffic simulation

Pourmoradnasseri,

Khoshkhah,

Hadachi

2023

IET Smart Cities

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“…Therefore, one of the major contributions that have been made is focused on extracting origin-destination matrices using CDR data as demonstrated in [ 7 , 15 , 16 ]. For example, in [ 16 ], the authors derive their approach to OD-matrix by relying on node-to-node transient OD matrices.…”

Section: Related Work and Cdr Data Limitationsmentioning

confidence: 99%

“…The results showed acceptable accuracy compared to traditional sources (questionnaires, surveys, and population censuses). Based on the obtained results, the method has found further applications in tourism [ 3 ], demographic analysis [ 4 ], epidemics modeling [ 5 ], urban planning [ 6 ], and transportation [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Gaussian Mixtures in a Multimodal Kalman Filter to Estimate the State of a Nonlinearly Moving System Using Sparse Inaccurate Measurements in a Cellular Radio Network

Lind

Hadachi

2023

Sensors

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Kalman filter is a well-established accuracy correction method in control, guidance, and navigation. With the popularity of mobile communication and ICT, Kalman Filter has been used in many new applications related to positioning based on spatiotemporal data from the cellular network. Despite the low accuracy compared to Global Positioning System, the method is an excellent supplement to other positioning technologies. It is often used in sensor fusion setups as a complementary source. One of the reasons for the Kalman Filter’s inaccuracy lies in naive radio coverage approximation techniques based on multivariate normal distributions assumed by previous studies. Therefore, in this paper, we evaluated those disadvantages and proposed a Gaussian mixtures model to address the non-arbitrary shape of the radio cells’ coverage area. Having incorporated the Gaussian mixtures model into Switching Kalman Filter, we achieved better accuracy in positioning within the cellular network.

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“…Let = ( ) be a normalized matrix with ∑ = 1, such that each entry suggests an estimated ratio of trips between th OD pair to all trips. Normalized trip distribution can be obtained from different data sources, such as travel surveys (Egu and Bonnel, 2020), census of population and housing (Arora et al, 2021), mobile data (Pourmoradnasseri, Khoshkhah, Lind and Hadachi, 2019;Hadachi, Pourmoradnasseri and Khoshkhah, 2020), public transport smart tickets (Mohamed, Côme, Oukhellou and Verleysen, 2016;Yong, Zheng, Mao, Tang, Gao and Liu, 2021), or geo-tagged social media posts (Gao, Yang, Yan, Hu, Janowicz and McKenzie, 2014). We refer to this input as normalized OD (NOD) matrix.…”

Section: Initial Nod Matrixmentioning

confidence: 99%

Real-Time Calibration of Disaggregated Traffic Demand

Pourmoradnasseri¹,

Khoshkhah²,

Hadachi³

2022

Preprint

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Real-time traffic demand estimation is essential for intelligent transportation systems and traffic forecasting in urban areas. Hence, this paper presents a simulation-based optimization framework for city-scale real-time estimation and calibration of dynamic demand models by focusing on disaggregated microsimulation in congested networks. The calibration approach is based on sequential optimization demand estimation for short time frames and uses a stream of traffic count data from IoT sensors on selected roads. The proposed method builds upon the standard bilevel optimization formulation. The upper-level optimization problem is presented as a bounded variable quadratic programming in each time frame, making it computationally tractable. The DTA (dynamic traffic assignment) problem at the lower level is approached based on the stochastic route choice model. For every time frame, the probabilistic parameters of the route choice model are obtained through several rounds of feedback loop between the OD optimization problem (at the upper level) and parallel samplings and simulations for DTA (at the lower level). At the end of each time frame, the microsimulation state of the network is transferred to the next time frame to ensure the temporal dependency and continuity of the estimations during the time. In addition, the algorithm's convergence is accelerated by applying the fixed-point method to road-segment travel times.The proposed sequential calibration model presents a drastic computational advantage over available methods by splitting the computations into short time frames and under the assumption that the demand in each time frame only depends on current and previous time frames. The model does not depend on reliable prior demand information. Moreover, with receiving the field measurements as a stream and the efficient time complexity of the algorithm in each time frame, the method successfully presents a solution for high-dimensional real-time and online applications. We validated the method with synthetic data and for a real-world case study in Tartu city, Estonia. The results show high accuracy under a tight computational budget.

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OD-Matrix Extraction based on Trajectory Reconstruction from Mobile Data

Cited by 7 publications

References 25 publications

Leveraging IoT data stream for near‐real‐time calibration of city‐scale microscopic traffic simulation

Leveraging IoT data stream for near‐real‐time calibration of city‐scale microscopic traffic simulation

Application of Gaussian Mixtures in a Multimodal Kalman Filter to Estimate the State of a Nonlinearly Moving System Using Sparse Inaccurate Measurements in a Cellular Radio Network

Real-Time Calibration of Disaggregated Traffic Demand

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