Towards better traffic volume estimation: Tackling both underdetermined and non-equilibrium problems via a correlation-adaptive graph convolution network

Traffic volume is an indispensable ingredient to provide fine-grained information for traffic management and control. However, due to limited deployment of traffic sensors, obtaining full-scale volume information is far from easy. Existing works on this topic primarily focus on improving the overall estimation accuracy of a particular method and ignore the underlying challenges of volume estimation, thereby having inferior performances on some critical tasks. This paper studies two key problems with regard to traffic volume estimation: (1) underdetermined traffic flows caused by undetected movements, and (2) non-equilibrium traffic flows arise from congestion propagation. Here we demonstrate a graph-based deep learning method that can offer a data-driven, model-free and correlation adaptive approach to tackle the above issues and perform accurate network-wide traffic volume estimation. Particularly, in order to quantify the dynamic and nonlinear relationships between traffic speed and volume for the estimation of underdetermined flows, a speed patternadaptive adjacent matrix based on graph attention is developed and integrated into the graph convolution process, to capture non-local correlations between sensors. To measure the impacts of non-equilibrium flows, a temporal masked and clipped attention combined with a gated temporal convolution layer is customized to capture time-asynchronous correlations between upstream and downstream sensors. We then evaluate our model on a real-world highway traffic volume dataset and compare it with several benchmark models. It is demonstrated that the proposed model achieves high estimation accuracy even under 20% sensor coverage rate and outperforms other baselines significantly, especially on underdetermined and non-equilibrium flow locations. Furthermore, comprehensive quantitative model analysis are also carried out to justify the model designs

Modeling Interactions Between Human Factors and Traffic Flow Characteristics

To serve research needs for traffic flow model development and highway safety enhancement, we model interactions between human factors and traffic flow character- istics, this topic includes methods on collecting data, modeling impacts of parameters on flow, and calibrating parameters on observed data. An example of successful traf- fic data collection is NGSIM data, which contains location, speed, and acceleration information of vehicles. An algorithm was designed to match and extract vehicles’ trajectory records, and utilize the extracted information for pattern recognition of lane changing maneuvers. This algorithm reads records from an NGSIM data set, pick out vehicles executing lane changing maneuvers, and note the corresponding time stamps. Also through matching these records by vehicle ID and time stamp, we obtain a map of vehicles when a lane changing is happening, thus calculating gaps and relative speeds becomes possible. An example of utilizing these information is pattern recognition on lane changing maneuvers. We analyze lane changing maneu- vers with speed data and gap data. The approach with speed data shows convincing results, as most lane changing vehicles have a descending and then ascending pattern on their speed profiles before executing the maneuver. On the other hand we can use collected data for calibrating parameters in traffic flow models. A heuristic method- ology is implemented to provide results with high accuracy, high efficiency and high robustness. Techniques include data aggregation and bisection analysis are applied in this approach to ensure achieving these goals and further requirements. Two traf- fic flow simulation models, Longitudinal Control Model (LCM) and Newell’s Model are calibrated by applying this approach using traffic data collected at Georgia 400 highway in July, 2003, with satisfying accuracy and robustness produced in a running time of less than 2 seconds. Meanwhile we can enhance human factors by applying new technologies, and connected vehicle is a good example which is rapidly devel- oping. Future vehicles will be able to communicate with each other which greatly improves drivers’ situational awareness. Consequently, drivers may be able to re- spond earlier to safety hazards before they manifest themselves in forms of imminent danger. Therefore, the overall effect of this technology can be attributed to drivers’ enhanced perception-reaction (P-R) capability which, in turn, translates to improved flow and capacity. However, it is critical to quantify such benefits before large-scale investment is made. In our research, a statistical transformation model is formulated to predict the probability distribution function of flow. By entering distributions of P-R time and enhanced P-R time, this model helps compare before and after distri- butions of traffic flow, based on which benefits of connected vehicles on traffic flow can be analyzed

DRACULA Microscopic Traffic Simulator

The DRACULA traffic simulator is a microscopic model in that the vehicles are individually represented. The movement of vehicles in the network are represented continuously and updated every one second. The network is modelled as a set of nodes and links which represent junctions and streets respectively. Vehicles are generated at their origins with a random headway distribution and are assigned a set of driver/vehicle characteristics (according to user-specified probabilities) and a fixed route. The movement of the vehicles on a network is governed by a car-following law, the gap acceptance rules and the traffic regulations at intersections. They can join a queue, change lane, discharge to another link or exit from the system. The traffic regulation at an intersection is actuated by traffic lights or right-of-way rules. The inputs to the simulation are network data, trip matrix, fixed-time signal plans, gap-acceptance and car-following parameters. Outputs are in forms of animated graphics and statistical measures of network performance. The program is written in C-language. All types of vehicle attributes are represented as one entity using the structure data type which provides a flexibility in storing and modifying various types of data. Attributes of nodes, links and lanes are also represented as structures. The large number of variables associated with vehicles and the network imply that the performance of the simulation depends on the size of the network and the total number of vehicles within the network at one time. The simulator can be applied in many areas of urban traffic control and management, such as detailed evaluation of traffic signal control strategies, environmental issues such as air pollution due to emission from vehicles in idling, accelerating, decelerating or cruising, and analyses of the effects of variable demand and supply upon the performance of a network

Techno-economic viability of integrating satellite communication in 4G networks to bridge the broadband digital divide

Bridging the broadband digital divide between urban and rural areas in Europe is one of the main targets of the Digital Agenda for Europe. Though many technological options are proposed in literature, satellite communication has been identified as the only possible solution for the most rural areas, due to its global coverage. However, deploying an end-to-end satellite solution might, in some cases, not be cost-effective. The aim of this study is to give insights into the economic effectiveness of integrating satellite communications into 4G networks in order to connect the most rural areas (also referred to as white areas) in Europe. To this end, this paper proposes a converged solution that combines satellite communication as a backhaul network with 4G as a fronthaul network to bring enhanced broadband connectivity to European rural areas, along with a techno-economic model to analyse the economic viability of this integration. The model is based on a Total Cost of Ownership (TCO) model for 5 years, taking into account both capital and operational expenditures, and aims to calculate the TCO as well as the Average Cost Per User (ACPU) for the studied scenarios. We evaluate the suggested model by simulating a hypothetical use case for two scenarios. The first scenario is based on a radio access network connecting to the 4G core network via a satellite link. Results for this scenario show high operational costs. In order to reduce these costs, we propose a second scenario, consisting of caching the popular content on the edge to reduce the traffic carried over the satellite link. This scenario demonstrates a significant operational cost decrease (more than 60%), which also means a significant ACPU decrease. We evaluate the robustness of the results by simulating for a range of population densities, hereby also providing an indication of the economic viability of our proposed solution across a wider range of areas

How TRAF-NETSIM Works.

This paper describes how TRAF-NETSIM works in detail. It is a review of the TRAF-NETSIM micro-simulation model, for use in the research topic "The Development of Queueing Simulation Procedures for Traffic in Bangkok". TRAF-NETSIM is a computer program for modelling of traffic in urban networks. It is written in the FORTRAN 77 computer language. It uses bit-manipulation mechanisms for "packing" and "unpacking" data and a program overlay structure to reduce the computer memory requirements of the program. The model is based on a fixed time, and discrete event simulation approach. The periodic scan method is used in the model with a time interval of one second. In the model, up to 16 different vehicle types with 4 different vehicle categories (car, carpool, bus and truck) can be identified. Also, the driver's behaviour (passive, normal, aggressive), pedestrians' movement, parking and blocking (eg a broken-down car) can be simulated. Moreover, it has the capability to simulate the effects of traffic control ranging from a simple stop sign controlled junction to a dynamic/real time control system. The effects of spillbacks can be simulated in detail. The estimation of fuel consumption and vehicle emissions are optional simulations. Car following and lane changing models are incorporated into TRAF-NETSIM. The outputs can be shown in US standard units, Metric units, or both

Guidelines for assessing pedestrian evacuation software applications

This paper serves to clearly identify and explain criteria to consider when evaluating the suitability of a pedestrian evacuation software application to assess the evacuation process of a building. Guidelines in the form of nine topic areas identify different modelling approaches adopted, as well as features / functionality provided by applications designed specifically for simulating the egress of pedestrians from inside a building. The paper concludes with a synopsis of these guidelines, identifying key questions (by topic area) to found an evaluation