A Real-Time Model for Pedestrian Flow Estimation in Urban Areas based on IoT Sensors

Khoshkhah, Kaveh; Pourmoradnasseri, Mozhgan; Hadachi, Amnir

doi:10.1109/itsc55140.2022.9922566

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…The availability of a real-time data stream provides the possibility of developing Abbreviations: N , Set of nodes in network; L, Set of inks in the network; K, Set of links with traffic counter; jKj, The number of traffic counters in the network; k, Traffic counter (sensor) index; M, The set of OD pairs; jMj, The number of OD pairs; m, OD pair index; c ¼ ck ð Þ, Vector of field traffic counts; c * ¼ c * k À � , Vector of estimated traffic counts; N = (η m ), Normalized OD trip distribution; X, Prior OD matrix (seed); X * ¼ x * m À � , Expected OD matrix; σ, Scaling factor in X ¼ σ � N; t ∈ {0, 1, 2, …}, Time frame index; Δ, Time frame length; δ, Departure time; R m , Set of routes between mth OD pair; i, Route index; θ i , Travel time of route i; θ ik , Travel time from origin to sensor k, on route i; E i , Expected number of trips on route i; A = (α km ), Assignment matrix; α km is the probability of a trip between mth OD pair crosses sensor k; τ, Vector of link travel times for a fixed time frame. detailed models for real-time estimation of mobility flow, even for light modes of transportation, such as bicycles [5] and pedestrians [6].…”

Section: Introductionmentioning

confidence: 99%

“…Many cities have taken this opportunity to collect and manage quality data for public and private use [3], facilitating the creation of a real‐time digital representation of the physical traffic [4]. The availability of a real‐time data stream provides the possibility of developing detailed models for real‐time estimation of mobility flow, even for light modes of transportation, such as bicycles [5] and pedestrians [6].…”

Section: Introductionmentioning

confidence: 99%

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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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show abstract

“…Many cities have taken this opportunity to collect and manage quality data for public and private use. The availability of a real-time data stream provides the possibility of developing detailed models for real-time estimation of mobility flow, even for light modes of transportation such as bicycles (Kumar, Kang, Kwon and Nikolaev, 2016) and pedestrians (Khoshkhah, Pourmoradnasseri and Hadachi, 2022a).…”

Section: Introductionmentioning

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Real-Time Calibration of Disaggregated Traffic Demand

Pourmoradnasseri

Khoshkhah

Hadachi

2022

Preprint

Self Cite

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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 bi-level 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.

show abstract

A Real-Time Model for Pedestrian Flow Estimation in Urban Areas based on IoT Sensors

Cited by 8 publications

References 20 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

Real-Time Calibration of Disaggregated Traffic Demand

Real-Time Calibration of Disaggregated Traffic Demand

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