Freeway speed-flow relationships under rain and congested conditions

A procedure to account for the impact of rain and congested conditions on the average speed estimates is provided in this study. Although the Highway Capacity Manual (HCM) provides some discussion on the impact of adverse weather on speed-flow relationships, these impacts are not quantified. Using data collected under rain and congested conditions, a procedure for estimating the average speed under these conditions is provided, which is an improvement over the existing HCM (2000) procedures. Using the speed-flow relationships provided in the HCM (2000) for basic freeway segments as a starting point, new numerical relationships suitable for New Jersey roadways are derived. The new speed-flow relationships can be used to estimate operating speed and level of service (LOS) for New Jersey roadways under rain and congested conditions. The findings are as follows: The speed-flow model developed in the research can be used to describe conditions under clear weather, rain, and congested conditions. The model reflects the fact that as flow increases, speed decreases under clear weather and rain conditions. Under congested conditions speed and flow operate on the lower or congested portion of the speed-flow model. In this case, as more vehicles are added, the discharge flow decreases and the speed also decreases. The speed under rain and congested conditions is higher than the speed under congested conditions. Under rain conditions the average speed decreases by about 0.05 mph when the precipitation level is 0.01 inches/hr. Both the speed-flow model developed in this research and the HCM (2000) show that the average speed under rain conditions seems to decrease slowly when the flow rate is less than 2000 vphpl. However, the rain adjustment factors, developed using individual roadways reflect the fact that the average speed under rain conditions seems to decrease significantly at low to medium flows and decreases slowly at medium to high flows

Platooning Safety and Capacity in Automated Electric Transportation

Automated Electric Transportation (AET) proposes a system of automated platooning vehicles electrically powered by the roadway via wireless inductive power transfer. This has the potential to provide roadway transportation that is less congested, more flexible, cleaner, safer, and faster than the current system. The focus of this research is to show how platooning can be accomplished in a safe manner and what capacities such an automated platooning system can achieve. To accomplish this, first two collision models are developed to show the performance of automated platoons during an emergency braking scenario: a stochastic model coded in Matlab/Simulink and a deterministic model with closed-form solutions. The necessary parameters for safe platooning are then defined: brake variances, communication delays, and maximum acceptable collision speeds. The two collision models are compared using the Student\u27s t-test to show their equivalence. It is shown that while the two do not yield identical results, in most cases the results of the deterministic model are more conservative than and reasonably close to the results of the deterministic model. The deterministic model is then used to develop a capacity model describing automated platooning flow as a function of speed and platoon size. For conditions where platooning is initially unsafe, three amelioration protocols are evaluated: brake derating, collaborative braking, and increasing the maximum acceptable collision speed. Automated platooning flow is evaluated for all of these scenarios, compared both with each other and with traditional roadway flow patterns. The results of these models show that when platooning is initially safe, very high vehicle flows are possible: for example, over 12,000 veh/hr for initial speeds of 30 m/s and 10 vehicle platoons. Varying system paramaters can have large ramifications for overall capacity. For example, autonomous (non-platooning) vehicles do not promise anywhere near this level, and in many cases struggle to approach the capacity of traditional roadways. Additionally, ensuring safety under an emergency braking standard requires very small communication delays and, most importantly, tight braking variances between the vehicles within a platoon. As proposed by AET, a single type of electric vehicle, combined with modern wireless communications, can make platooning safer than was previously possible without requiring amelioration. Both brake derating and collaborative braking can make platooning safer, but they reduce capacity and may not be practical for real-world implementation. Stricter versions of these, cumulative brake derating and exponential collaborative braking, are also evaluated. Both can degrade capacity to near current roadway levels, especially if a large degree of amelioration is required. Increasing maximum acceptable collision speed, such as through designing vehicles to better withstand rear-end collisions, shows more promise in enabling safe intraplatoon interactions, especially for scenarios with small communication delays (i.e. under 50 ms)

Intelligent Computational Transportation

Transportation is commonplace around our world. Numerous researchers dedicate great efforts to vast transportation research topics. The purpose of this dissertation is to investigate and address a couple of transportation problems with respect to geographic discretization, pavement surface automatic examination, and traffic ow simulation, using advanced computational technologies. Many applications require a discretized 2D geographic map such that local information can be accessed efficiently. For example, map matching, which aligns a sequence of observed positions to a real-world road network, needs to find all the nearby road segments to the individual positions. To this end, the map is discretized by cells and each cell retains a list of road segments coincident with this cell. An efficient method is proposed to form such lists for the cells without costly overlapping tests. Furthermore, the method can be easily extended to 3D scenarios for fast triangle mesh voxelization. Pavement surface distress conditions are critical inputs for quantifying roadway infrastructure serviceability. Existing computer-aided automatic examination techniques are mainly based on 2D image analysis or 3D georeferenced data set. The disadvantage of information losses or extremely high costs impedes their effectiveness iv and applicability. In this study, a cost-effective Kinect-based approach is proposed for 3D pavement surface reconstruction and cracking recognition. Various cracking measurements such as alligator cracking, traverse cracking, longitudinal cracking, etc., are identified and recognized for their severity examinations based on associated geometrical features. Smart transportation is one of the core components in modern urbanization processes. Under this context, the Connected Autonomous Vehicle (CAV) system presents a promising solution towards the enhanced traffic safety and mobility through state-of-the-art wireless communications and autonomous driving techniques. Due to the different nature between the CAVs and the conventional Human- Driven-Vehicles (HDVs), it is believed that CAV-enabled transportation systems will revolutionize the existing understanding of network-wide traffic operations and re-establish traffic ow theory. This study presents a new continuum dynamics model for the future CAV-enabled traffic system, realized by encapsulating mutually-coupled vehicle interactions using virtual internal and external forces. A Smoothed Particle Hydrodynamics (SPH)-based numerical simulation and an interactive traffic visualization framework are also developed

Dynamic impact modeling as a road transport crisis management support tool

Crisis management must provide data to allow for real-time decision-making. Accurate data is especially needed to minimize the risk of critical infrastructure failure. Research into the possible impacts of critical infrastructure failure is a part of developing a functional and secure infrastructure for each nation state. Road transport is one such sector that has a significant impact on its functions. When this fails, there may be a cascading spread of impacts on the energy, health, and other sectors. In this regard, this paper focuses on the dynamic modeling of the impacts of critical road infrastructure failures. It proposes a dynamic modeling system based on a stochastic approach. Its essence is the macroscopic model-based comparative analysis of a road with a critical element and detour roads. The outputs of this system are planning documents that determine the impacts of functional parameter degradation on detour roads-not only applicable in decision-making concerning the selection of the optimal detour road, but also as a support mechanism in minimising possible risks. In this article we aim to expand the extent of knowledge in the Crisis management and critical infrastructure protection in the road transport sector fields.Ministry of the Interior of the Czech Republic [VI20152019049]; Technology Agency of the Czech Republic [TE01020168]; VSB-Technical University of Ostrava [SP2019/96]Ministerstvo Vnitra České Republiky: VI20152019049; Technology Agency of the Czech Republic, TACR: TE01020168; Vysoká Škola Bánská - Technická Univerzita Ostrava: SP2019/9

Revisiting the empirical fundamental relationship of traffic flow for highways using a causal econometric approach

The fundamental relationship of traffic flow is empirically estimated by fitting a regression curve to a cloud of observations of traffic variables. Such estimates, however, may suffer from the confounding/endogeneity bias due to omitted variables such as driving behaviour and weather. To this end, this paper adopts a causal approach to obtain an unbiased estimate of the fundamental flow-density relationship using traffic detector data. In particular, we apply a Bayesian non-parametric spline-based regression approach with instrumental variables to adjust for the aforementioned confounding bias. The proposed approach is benchmarked against standard curve-fitting methods in estimating the flow-density relationship for three highway bottlenecks in the United States. Our empirical results suggest that the saturated (or hypercongested) regime of the estimated flow-density relationship using correlational curve fitting methods may be severely biased, which in turn leads to biased estimates of important traffic control inputs such as capacity and capacity-drop. We emphasise that our causal approach is based on the physical laws of vehicle movement in a traffic stream as opposed to a demand-supply framework adopted in the economics literature. By doing so, we also aim to conciliate the engineering and economics approaches to this empirical problem. Our results, thus, have important implications both for traffic engineers and transport economists

Statistical Physics of Vehicular Traffic and Some Related Systems

In the so-called "microscopic" models of vehicular traffic, attention is paid explicitly to each individual vehicle each of which is represented by a "particle"; the nature of the "interactions" among these particles is determined by the way the vehicles influence each others' movement. Therefore, vehicular traffic, modeled as a system of interacting "particles" driven far from equilibrium, offers the possibility to study various fundamental aspects of truly nonequilibrium systems which are of current interest in statistical physics. Analytical as well as numerical techniques of statistical physics are being used to study these models to understand rich variety of physical phenomena exhibited by vehicular traffic. Some of these phenomena, observed in vehicular traffic under different circumstances, include transitions from one dynamical phase to another, criticality and self-organized criticality, metastability and hysteresis, phase-segregation, etc. In this critical review, written from the perspective of statistical physics, we explain the guiding principles behind all the main theoretical approaches. But we present detailed discussions on the results obtained mainly from the so-called "particle-hopping" models, particularly emphasizing those which have been formulated in recent years using the language of cellular automata.Comment: 170 pages, Latex, figures include

Empirical Estimation of a Macroscopic Fundamental Diagram (MFD) for the City of Cape Town Freeway Network

The City of Cape Town is the most congested city in South Africa, with Johannesburg coming in second. Capetonians are spending 75% more time in traffic because of the congestion during peak hours, thus reducing time spent on leisure and other activities. Due to population growth, increasing car ownership and declining capacity of rail infrastructure, Cape Town's road infrastructure will continue to be under severe pressure if the status quo is maintained. Research shows that congestion levels in urban areas are key factors in determining the effectiveness and productivity of the transport system. Traffic congestion poses a threat to the economy and the environment. Increasing corridors' capacity by increasing the number of lanes does not necessarily solve the problem. Effective urban traffic management and efficient utilization of existing infrastructure are critical in creating sustainable solutions to congestion problems. To achieve this, it is important that appropriate urban-scale models and monitoring strategies are put in place. Effective traffic management and monitoring strategies require accurate characterization of the traffic state of an urban-scale network. Several approaches, including kinetic wave theory and cell transmission models or macroscopic traffic simulation models, have been proposed and developed to describe the traffic state of an urban-scale network. However, these approaches are limited and require significant amounts of computational time and effort. The application of macroscopic fundamental diagram (herein referred to as MFD) to characterize the state of an urban-scale network has thus far proven to be more effective than other approaches. MFD represents the state of urban traffic by defining the traffic throughput of an area at given traffic densities. It describes the characteristics and dynamics of urban-scale traffic conditions, allowing for improved and sustainable urban scale traffic management and monitoring strategies. Against this backdrop, the existence of MFD for the City of Cape Town (CoCT) urbanscale network is yet to be established and the implications yet to be understood, as in other parts of the world. The main aim of this research was, therefore, to empirically estimate the macroscopic fundamental diagram for the CoCT's freeway network and analyse its observed features. To achieve this, observed data of 5 minutes periods for the month of May 2019 was used to estimate the MFD. The results confirmed that when the chaotic scatter-plots of flow and density from individual fixed loop detectors were aggregated the scatter nearly disappeared and points grouped neatly to form a clearly defined free-flow state, critical state and the formation of hysteresis loops past the critical density corresponding with the network observed maximum flow. Further analysis of the MFDs showed that a single hysteresis loop always forms past the critical density during the evening peak in a weekday MFD. However, it was inconclusive during the morning peak period in weekday MFDs. Lastly, an explicit hysteresis loop seldom appears in a Saturday MFD when the peak of traffic demand is lower than on weekdays. In order to understand the dynamics of the congestion spread, the freeway network was partitioned into penetrating highways network and the ring highway network. The results showed that the maximum flows observed for the two sub-networks were significantly different (943 veh/hr/lane for the penetrating highways network and 1539 veh/hr/lane for the ring highway network). The penetrating highways network's MFD indicated the presence of congestion in the network whereas the ring highway network indicated only the free-flow state (no indication of congestion) during peak periods. The congestion seen on the penetrating highways network was found not to be sufficiently spread on those highways. On the 24th May, congestion on the penetrating highway network was observed during both the morning and evening peak periods, whereas on the 31st May congestion was observed mainly during the evening peak period, with hysteresis-like shape. These observations confirmed that congestion during peak periods is not homogenously spread across the entire network, certain areas are more congested than others, hence the observed formation of hysteresis loops and slight scatters. Lastly, the hysteresis loops observed in the penetrating highways network's MFD was further characterized in terms of their shape and size. First, the results showed that the slight scatter and hysteresis patterns observed in penetrating highways network MFD's vary in size and shape across different days. The shapes of the hysteresis loops observed during both the morning and evening peak periods, were type H2 hysteresis loops, signifying a stable recovery of the network with the average network flow remaining unchanged as average network density decreases during the recovery. Characterization of the size of the observed hysteresis loops showed that the drop of the hysteresis (an indicator of network level of instability during recovery phase) was smaller, signifying a more stable network traffic and homogenous distribution of congestion during the recovery phase

Innovative Modelling Approaches for the Design, Operation and Control of Complex Energy Systems with Application to Underground Infrastructures

The ventilations systems play a key role in underground infrastructures for health and safety of occupants during normal operation as well as during accidents. Their performances are affected by selection of the optimal design, operation and control that is investigated by predicting air flow. The calculation of ventilation flows and their interaction with fires can be done with different modelling approaches that differ in the accuracy and in the required resources. The 3D computational fluid dynamics (CFD) tools approximate the flow behaviour with a great accuracy but they require high computational resources. The one dimensional (1D) models allow a compact description of the system with a low computational time but they are unsuitable to simulate thermal fluid-dynamic scenarios characterized by turbulence and gradients. Innovative tools are necessary in order to make the analysis and optimization of these systems possible and accurate in a reasonable time. This can be achieved both with appropriate numerical approaches to the full domain as the model order reduction techniques and with the domain decompositions methods as the multiscale physical decomposition technique. The reduced order mode techniques as the proper orthogonal decomposition (POD) is based on the snapshots method provides an optimal linear basis for the reconstruction of multidimensional data. This technique has been applied to non-dimensional equations in order to produce a reduced model not depending on the geometry, source terms, boundary conditions and initial conditions. This type of modelling is adapted to the optimization strategies of the design and operation allowing to explore several configuration in reduced times, and for the real time simulation in the control algorithms. The physical decomposition achieved through multiscale approaches uses the accuracy of the CFD code in the near field e.g. the region close to the fire source, and takes advantage of the low computational cost of the 1-D model in the region where gradients in the transversal direction are negligible. In last years, the multiscale approach has been proposed for the analysis of tunnel ventilation. Among the several CFD codes used in this field, the Fire Dynamic Simulator (FDS) is suitable for the multiscale modelling. This is an open source CFD package developed by NIST and VTT and presents the HVAC routine in which the conservation equations of mass, energy and momentum are implemented. Currently, the HVAC module does not allow one to consider heat and mass transfer, which significanltly limits the applications. For these reasons a multiscale simulator has been created through the fully integration of a 1D continuity, momentum, energy and mass transport equation in FDS modifying its source codes. The multiscale simulator thus obtained, is based on a direct coupling by means of a Dirichlet-Neumann strategy. At each 1-D-CFD interface, the exchange flow information occurs prescribing thermo-fluid dynamic boundary conditions. The 1-D mass transport equation computes the diffusion of the exhaust gas from the CFD domain and the relative concentration that is particularly interesting in the case of back layering of smoke. The global convergence of the boundary conditions at each 1-D-CFD interface has been analyzed by monitoring the evolution of thermo-fluid dynamic variables (temperature, velocity, pressure and concentration. The multiscale simulator is suitable for parametric and sensitivity studies of the design and the operation ventilation and fire safety systems. This new tool will be available for all the scientific community. In this thesis, Chapter 1 provides a general introduction to the role of the system ventilation in underground infrastructures and to the innovative modelling strategies proposed for these systems. Chapter 2 offers a description of the 1D network modelling, its fluid-dynamic application to the Frejus tunnel and its thermal application to ground heat exchangers. In Chapter 3, the proper orthogonal decomposition method is presented and its application to the optimal control of the sanitary ventilation for the Padornelo Tunnel is discussed. To demonstrate the applicability of POD method in other fields, boreholes thermal energy storage systems have been considered in same chapter. In particular, a multi-objective optimization strategy is applied to investigate the optimal design of these system and an optimization algorithm for the operation is proposed. Chapter 4 describes the multiscale approach and the relative simulator. The new open tool is used for modeling the ventilation system of the Monte Cuneo road tunnel in case of fire. Results show that in the case of the current configuration of the ventilation system, depending on the atmospheric conditions at portals, smoke might not be fully confined. Significant improvements in terms of safety conditions can be achieved through increase of in smoke extraction, which requires the installation of large dumpers and of deflectors on the jet fans. The developed tool shows to be particularly effective in such analysis, also concerning the evaluation of local conditions for people evacuation and fire-brigades operation