Aggregation, disaggregation and decomposition methods in traffic assignment: historical perspectives and new trends

Raadsen, Mark P.H.; Bliemer, Michiel C.J.; Bell, Michael G.H.

doi:10.1016/j.trb.2020.06.008

Cited by 11 publications

(4 citation statements)

References 88 publications

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“…The advantage of this approach is that it is much more computationally efficient than MILPbased models while maintaining accurate representation of traffic dynamics such as congestion. The disadvantage is that it may not derive itinerary of individual vehicles and travelers; however, this limitation is common in most of conventional traffic assignment problems [26].…”

Section: A Operational Aspects Of Sav Systemsmentioning

confidence: 99%

Multi-Objective Linear Optimization Problem for Strategic Planning of Shared Autonomous Vehicle Operation and Infrastructure Design

Seo

Asakura

2022

IEEE Trans. Intell. Transport. Syst.

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This study proposes a unified optimization framework for strategic planning of shared autonomous vehicle (SAV) systems that explicitly and endogenously considers their operational aspects based on macroscopic dynamic traffic assignment. Specifically, the proposed model optimizes fleet size, road network design, and parking space allocation of an SAV system with optimized SAVs' dynamic routing with passenger pickup/delivery and ridesharing. It is formulated as a multi-objective optimization problem that simultaneously minimizes total travel time of travelers, total distance traveled by SAVs, total number of SAVs, and infrastructure construction cost; thus, both the user-side cost and the system-side cost are taken into account, and their trade-off relations can be explicitly investigated. Furthermore, the problem is formulated as a linear programming problem, making it easy to solve. By leveraging the linearity, we mathematically derive a useful property of the problem: introduction of ridesharing can weakly monotonically and simultaneously decrease the user-side cost and system-side cost. The proposed model is evaluated by applying it to actual travel records obtained from New York City taxi data.

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Section: A Operational Aspects Of Sav Systemsmentioning

confidence: 99%

Multi-Objective Linear Optimization Problem for Strategic Planning of Shared Autonomous Vehicle Operation and Infrastructure Design

Seo

Asakura

2022

IEEE Trans. Intell. Transport. Syst.

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“…One could train a machine learning agent with the privatized query response to predict travel behavior for the next day. Authors in [42] studied major route and busy traffic with an aggregated bicycle dataset. Our mechanism perturbs the origin/destination of trajectories before aggregation using the density and attributes of the localized links and preserves the main traffic routes.…”

Section: Differentially Private Movement Datasetsmentioning

confidence: 99%

Adaptive Differential Privacy Mechanism for Aggregated Mobility Dataset

Haydari¹,

Zhang²,

Chuah³

et al. 2021

Preprint

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Location data is collected from users continuously to acquire user mobility patterns. Releasing the user trajectories may compromise user privacy. Therefore, the general practice is to release aggregated location datasets. However, private information may still be inferred from an aggregated version of location trajectories. Differential privacy (DP) protects the query output against inference attacks regardless of background knowledge. This paper presents a differential privacy-based privacy model that protects the user's origins and destinations at the aggregated level. This is achieved by injecting Planar Laplace noise to the user origin and destination GPS points. The noisy GPS points are then transformed to a link representation using a link-matching algorithm. Finally, the link trajectories form an aggregated mobility network. The injected noise level is selected adaptively, by considering the link density of the location and the functional category of the localized links. Compared to the different baseline models, including a k-anonymity method, our differential privacy-based aggregation model offers closer query responses to the raw data in terms of aggregate statistics at both the network and trajectory-levels with max 4% deviation from the baseline. Beyond link aggregation and spatial noise injection, temporal aggregation can also provide a degree of privacy and a discussion of temporal aggregation requirements is presented.

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“…Dynamic traffic assignment (DTA) has received increasing attention from scholars in the field of intelligent transportation systems [7][8][9][10]. The purpose of traffic network analysis is to describe the relationship between time-varying networks and users' travel behaviour patterns in a rational way [11], and the model analysis results can optimize network traffic assignment, alleviate urban congestion, and provide a reference for policy measures of regional road network planning in most cities in China.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Traffic Assignment Model Based on GPS Data and Point of Interest (POI) in Shanghai

Song

Zheng

Wang

et al. 2021

Sensors

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Dynamic traffic flow, which can facilitate the efficient operation of traffic road networks, is an important prerequisite for the application of reasonable assignment of traffic demands in an urban road network. In order to improve the accuracy of dynamic traffic flow assignment, this paper proposes a dynamic traffic flow assignment model based on GPS trajectory data and the influence of POI. First, this paper explores the impact patterns of POI on regional road network congestion during peak hours through qualitative and quantitative analysis. Then, based on the user equilibrium theory, a dynamic traffic flow assignment model, in which the effect of POI on links is reflected using the link-node impedance function, is proposed. Finally, the accuracy of the model is validated by the GPS trajectory data and origin–destination (OD) traffic data of motor vehicles in Xuhui District, Shanghai, China. The results show that the model can be used to coordinate and optimize the traffic assignment of the regional road network under the influence of POI during peak hours and alleviate the congestion of the road network. The findings can provide a powerful reference for developing scientific and rational traffic assignment decisions and management strategies for urban road network traffic.

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Aggregation, disaggregation and decomposition methods in traffic assignment: historical perspectives and new trends

Cited by 11 publications

References 88 publications

Multi-Objective Linear Optimization Problem for Strategic Planning of Shared Autonomous Vehicle Operation and Infrastructure Design

Multi-Objective Linear Optimization Problem for Strategic Planning of Shared Autonomous Vehicle Operation and Infrastructure Design

Adaptive Differential Privacy Mechanism for Aggregated Mobility Dataset

Dynamic Traffic Assignment Model Based on GPS Data and Point of Interest (POI) in Shanghai

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