Development and Empirical Study of Real-Time Simulation-Based Dynamic Traffic Assignment Model

This research aims at developing a real-time simulation-based dynamic traffic model for mixed traffic flow conditions for ATMS/ATIS applications. The real-time model is implemented based on rolling horizon approach. The rolling horizon is advanced for each stage, thus real-time data can be incorporated within the framework. In order to predict normative as well as predictive information, a simulation-based DTA model is employed within each stage. Empirical data for freeways, including flows from toll stations and vehicle detectors, are used to validate the model in a real-time environment.

Application of a simulation-based dynamic traffic assignment model

Dynamic traffic assignment (DTA) applications require traffic signal control data that are typically difficult to obtain and cumbersome to code in the required format. In addition, the evaluation of future scenarios requires future traffic signal settings consistent with the forecast demand. These signal settings are not known a priori and are costly to estimate. Intuitively, future signal timings need to be reasonably optimized so as to represent what the traffic management agency will do. In the literature, integration between traffic control and DTA models has been formulated as a bi-level or single-level optimization problem with system or user optimal constraints. Most existing solution procedures require certain nested structure with an inner-loop algorithm solving the problem of user-equilibrium or system-optimal assignment and the outer-loop algorithm searching for the optimal signal-timing settings. Most of these solution approaches remain only research tools without practical use because of computational intractability. This research proposes an efficient solution algorithm to the problem. An adaptive traffic signal control model is embedded in a simulation-based DTA model. For each inbound approach at an intersection of interest, the adaptive model uses upstream information and a dynamic rolling-horizon approach to project traffic flow conditions for a dynamic but short (projection) period. The adaptive model provides the signal settings during the entire process of traffic flow simulation and for every iteration of the solution algorithm. Thus, during the entire solution process, the experienced travel times and resulting traffic assignment flows are based on the adaptive (demand-responsive) signal settings, allowing the DTA flows and the adaptive signal settings to be generated simultaneously in a single-loop algorithmic structure. Simulation experiments illustrate the capabilities of the proposed approach.

Distance-based time-dependent optimal ratio control scheme (TORCS) in congested mixed autonomy networks

Joint routing and pricing control in congested mixed autonomy networks

A cell-based dynamic traffic assignment model: Formulation and properties

Improving fuel consumption and CO2 emissions calculations in urban areas by coupling a dynamic micro traffic model with an instantaneous emissions model

Comparative Study of Pedestrian Crossing Behaviour at Uncontrolled Intersection and Midblock Locations

Road traffic impedance model is a difficult and critical point in urban traffic assignment and route guidance. The paper takes a signalized intersection as the research object. On the basis of traditional traffic wave theory including the implementation of traffic wave model and the analysis of vehicles’ gathering and dissipating, the road traffic impedance model is researched by determining the basic travel time and waiting delay time. Numerical example results have proved that the proposed model in this paper has received better calculation performance compared to existing model, especially in flat hours. The values of mean absolute percentage error (MAPE) and mean absolute deviation (MAD) are separately reduced by 3.78% and 2.62 s. It shows that the proposed model has feasibility and availability in road traffic impedance under intersection signal.

Dynamic traffic simulation models are frequently used to support decisions when planning an evacuation. This contribution reviews the different (mathematical) model formulations underlying these traffic simulation models used in evacuation studies and the behavioural assumptions that are made. The appropriateness of these behavioural assumptions is elaborated on in light of the current consensus on evacuation travel behaviour, based on the view from the social sciences as well as empirical studies on evacuation behaviour. The focus lies on how travellers’ decisions are predicted through simulation regarding the choice to evacuate, departure time choice, destination choice, and route choice. For the evacuation participation and departure time choice we argue in favour of the simultaneous approach to dynamic evacuation demand prediction using the repeated binary logit model. For the destination choice we show how further research is needed to generalize the current preliminary findings on the location-type specific destination choice models. For the evacuation route choice we argue in favour of hybrid route choice models that enable both following instructed routes and en-route switches. Within each of these discussions, we point at current limitations and make corresponding suggestions on promising future research directions.

Roundabout Capacity in Heterogeneous Traffic Condition: Modification of HCM Equation and Calibration

Impacts of bus holding strategies on the performance of mixed traffic system

Recent research has investigated the effects of var iable speed limits (VSL) on traffic flow processes. Findings show that VSL profoundly impact lane flow distribution (LFD) and increase the use of the slow lane by reducing the speed difference between the slow lane and the overtaking lanes. Finally, VSL reduce the capacity drop phenomenon on multi-lane sections and significantly increase the proportion of flow on the slow lane. These results highlight the major impact of LFD on the ca pacity of multi-lane sections. The paper presents a new analytical model that esti mates the capacity of multi-lane sections and repro duces the capacity-drop caused by LFD characteristics. It can be applied to estimate the capacity of motorway se ctions with two or three lanes under common traffic condit ions; it can also estimate the impact of VSL on the capacity of multi-lane sections. The multi-lane capacity model is coupled with a dyn amic mesoscopic traffic flow model initially design ed for network applications: the meso-LWR model. The resulting dynamic traffic flow model is relevant for mot orway network applications and VSL evaluation. The dynamic model is implemented on a French motorway site. The results show the ability of the model to reproduce actual traffic situations (capacity, congestion per iod, etc.) without VSL, including the amplitude of capacity dr op phenomenon caused by the under-use of the slow lane. It also shows the ability of the model to estimate the effect of VSL on future traffic flow conditions.

Impacts of vehicle electrification on large-scale transportation and charging infrastructure: A dynamic network modeling approach

Urban air quality simulation is an important tool to understand the impacts of air pollution. However, the simulations are often computationally expensive, and require extensive data on pollutant sources. Data on road traffic pollution, often the predominant source, can be obtained through sparse measurements, or through simulation of traffic and emissions. Modeling chains combine the simulations of multiple models to provide the most accurate representation possible, however the need to solve multiple models for each simulation increases computational costs even more. In this paper we construct a meta-modeling chain for urban atmospheric pollution, from dynamic traffic modeling to air pollution modeling. Reduced basis methods (RBM) aim to compute a cheap and accurate approximation of a physical state using approximation spaces made of a suitable sample of solutions to the model. One of the keys of these techniques is the decomposition of the computational work into an expensive one-time offline stage and a low-cost parameter-dependent online stage. Traditional RBMs require modifying the assembly routines of the computational code, an intrusive procedure which may be impossible in cases of operational model codes. We propose a non-intrusive reduced order scheme, and study its application to a full chain of operational models. Reduced basis are constructed using principal component analysis (PCA), and the concentration fields are approximated as projections onto this reduced space. We use statistical emulation to approximate projection coefficients in a non-intrusive manner. We apply a multi-level meta-modeling technique to a chain using the dynamic traffic assignment model LADTA, the emissions database COPERT IV, and the urban dispersion-reaction air quality model SIRANE to a case study on the city of Clermont-Ferrand with over 45, 000 daily traffic observations, a 47, 000-link road network, a simulation domain covering 180,text {km}^2. We assess the results using hourly NO_2 concentration observations measured at stations in the agglomeration. Computational times are reduced from nearly 3 h per simulation to under 0.1 s, while maintaining accuracy comparable to the original models. The low cost of the meta-model chain and its non-intrusive character demonstrate the versatility of the method, and the utility for long-term or many-query air quality studies such as epidemiological inquiry or uncertainty quantification.