A general formulation for multi-modal dynamic traffic assignment considering multi-class vehicles, public transit and parking

Pi, Xidong; Ma, Wei; Qian, Sean

doi:10.1016/j.trpro.2019.05.047

Cited by 10 publications

(10 citation statements)

References 35 publications

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“…For the traffic context, the most widely used features are lane markings, surrounding vehicle positions, digital maps, and Global Positioning System (GPS), etc. [31]- [33]. Important driver behavioral features consist of head motion, eye gaze, body gestures, and even the Electroencephalography (EEG), etc.…”

Section: B Literature Reviewmentioning

confidence: 99%

An ensemble deep learning approach for driver lane change intention inference

Xing

Wang

Cao

et al. 2020

Transportation Research Part C: Emerging Technologies

145

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With the rapid development of intelligent vehicles, drivers are increasingly likely to share their control authorities with the intelligent control unit. For building an efficient Advanced Driver Assistance Systems (ADAS) and shared-control systems, the vehicle needs to understand the drivers' intent and their activities to generate assistant and collaborative control strategies. In this study, a driver intention inference system that focuses on the highway lane change maneuvers is proposed. First, a high-level driver intention mechanism and framework are introduced. Then, a vision-based intention inference system is proposed, which captures the multi-modal signals based on multiple low-cost cameras and the VBOX vehicle data acquisition system. A novel ensemble bi-directional recurrent neural network (RNN) model with Long Short-Term Memory (LSTM) units is proposed to deal with the time-series driving sequence and the temporal behavioral patterns. Naturalistic highway driving data that consists of lane-keeping, left and right lane change maneuvers are collected and used for model construction and evaluation. Furthermore, the driver's pre-maneuver activities are statistically analyzed. It is found that for situation-aware, drivers usually check the mirrors for more than six seconds before they initiate the lane change maneuver, and the time interval between steering the handwheel and crossing the lane is about 2 s on average. Finally, hypothesis testing is conducted to show the significant improvement of the proposed algorithm over existing ones. With five-fold cross-validation, the EBiLSTM model achieves an average accuracy of 96.1% for the intention that is inferred 0.5 s before the maneuver starts.

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Section: B Literature Reviewmentioning

confidence: 99%

An ensemble deep learning approach for driver lane change intention inference

Xing

Wang

Cao

et al. 2020

Transportation Research Part C: Emerging Technologies

145

View full text Add to dashboard Cite

show abstract

“…In that work, the intermodal network is translated into an augmented-state network in which transfer rules and transfer probabilities between the different transport modes are defined. In [25], a stochastic model is proposed in order to account for the reliability of the chosen transport modes and paths, while in [26] different modes are considered, onlydriving, carpooling, ride-hailing, public transit and park-andride, for a urban transportation system including private cars, freight trucks, buses, and so on.…”

Section: Literature Reviewmentioning

confidence: 99%

Two-Stage Multiclass Modeling Approach for Intermodal Rail-Road Transport Networks

Pasquale

Siri

et al. 2022

IEEE Access

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“…The mesoscopic method allows modelling more details with respect to the macroscopic model, by enabling the determination of individual trip times and waiting times. Mesoscopic simulations are slower compared with macroscopic assignments but still fast enough to simulate large urban areas [13][14][15][16]. Mesoscopic models are also used to determine a dynamic user equilibrium (DUE) by running simulations iteratively while updating link travel times [10].…”

Section: Introductionmentioning

confidence: 99%

Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data

Schweizer

Poliziani

Rupi

et al. 2021

IJGI

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A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built and validated for the city of Bologna (Italy) during the morning peak hour. Large-scale microsimulations enable the evaluation of city-wide effects of novel and complex transport technologies and services, such as intelligent traffic lights or shared autonomous vehicles. Large-scale microsimulations can be seen as an interdisciplinary project where transport planners and technology developers can work together on the same scenario; big data from OpenStreetMap, traffic surveys, GPS traces, traffic counts and transit details are merged into a unique transport scenario. The employed activity-based demand model is able to simulate and evaluate door-to-door trip times while testing different mobility strategies. Indeed, a utility-based mode choice model is calibrated that matches the official modal split. The scenario is implemented and analyzed with the software SUMOPy/SUMO which is an open source software, available on GitHub. The simulated traffic flows are compared with flows from traffic counters using different indicators. The determination coefficient has been 0.7 for larger roads (width greater than seven meters). The present work shows that it is possible to build realistic microsimulation scenarios for larger urban areas. A higher precision of the results could be achieved by using more coherent data and by merging different data sources.

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A general formulation for multi-modal dynamic traffic assignment considering multi-class vehicles, public transit and parking

Cited by 10 publications

References 35 publications

An ensemble deep learning approach for driver lane change intention inference

An ensemble deep learning approach for driver lane change intention inference

Two-Stage Multiclass Modeling Approach for Intermodal Rail-Road Transport Networks

Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data

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