Estimation of the network capacity for multimodal urban systems

As more people through different modes compete for the limited urban space that is set aside to serve transport, there is an increasing need to understand details of how this space is used and how it can be managed to improve accessibility for everyone. Ultimately, an important goal is to understand what sustainable level of mobility cities of different structures can achieve. Understanding these outcomes parametrically for all possible city structures and mixes of transport modes would inform the decision making process, thereby helping cities achieve their sustainability goals. In this paper we focus on the network capacity of multimodal systems with motorized traffic and extra emphasis in buses. More specifically, we propose to study how the throughput of passengers and vehicles depends on the geometrical and operational characteristics of the system, the level of congestion and the interactions between different modes. A methodology to estimate a macroscopic fundamental diagram and network capacity of cities with mixed-traffic bus-car lanes or with individual bus-only lanes is developed and examples for different city topologies are provided. The analysis is based on realistic macroscopic models of congestion dynamics and can be implemented with readily available data. (C) 2011 Published by Elsevier Ltd.

A systematic analysis of multimodal transport systems with road space distribution and responsive bus service

Nikolaos Geroliminis, Fangni Zhang, Nan Zheng

A smart design of transport systems involves efficient use and allocation of the limited urban road capacity in the multimodal environment. This paper intends to understand the system-wide effect of dividing the road space to the private and public transport modes and how the public transport service provider responds to the space changes. To this end, the bimodal dynamic user equilibrium is formulated for separated road space. The Macroscopic Fundamental Diagram (MFD) model is employed to depict the dynamics of the automobile traffic for its state-dependent feature, its inclusion of hypercongestion, and its advantage of capturing network topology. The delay of a bus trip depends on the running speed which is in turn affected by bus lane capacity and ridership. Within the proposed bimodal framework, the steady-state equilibrium traffic characteristics and the optimal bus fare and service frequency are analytically derived. The counter-intuitive properties of traffic condition, modal split, and behavior of bus operator in the hypercongestion are identified. To understand the interaction between the transport authority (for system benefit maximization) and the bus operator (for its own benefit maximization), we examine how the bus operator responds to space changes and how the system benefit is influenced with the road space allocation. With responsive bus service, the condition, under which expanding bus lane capacity is beneficial to the system as a whole, has been analytically established. Then the model is applied to the dynamic framework where the space allocation changes with varying demand and demand-responsive bus service. We compare the optimal bus services under different economic objectives, evaluate the system performance of the bimodal network, and explore the dynamic space allocation strategy for the sake of social welfare maximization.

2018

Traffic modeling, forecasting and assignment in large-scale networks

Mehmet Yildirimoglu

Today, the development and evaluation of traffic management strategies heavily relies on microscopic traffic simulation models. In case detailed input (i.e. od matrix, signal timings, etc.) is extracted and incorporated in these simulators, they can provide valuable traffic state predictions. However, as this type of information is almost never available at the large-scale and traffic represents chaotic behavior in saturated networks, microscopic simulation models remain intractable and unstable. An alternative is a recently discovered network traffic model; macroscopic fundamental diagram (MFD). Nevertheless, large-scale traffic management strategies remain a big challenge partly due to unpredictability of choices of travelers (e.g. route, departure time and mode choice). Part I of the thesis is an attempt to fill this gap. Chapter 2, 3 and 4 elaborate new aspects of large-scale traffic modeling, and integrate route choice behavior into the modeling. Chapter 2 proposes a dynamic traffic assignment (DTA) model to establish equilibrium conditions in multi-region urban networks where the modeling is done through MFD dynamics. The method handles the stochastic components of the aggregated model through a sampling approach. In addition, the assignment model enables us to consider the response of drivers to changing traffic conditions in an aggregated modeling framework. Chapter 3 extends the DTA model presented in Chapter 2 to a route guidance system, where drivers are given a sequence of subregions to follow. Two aggregated models, region- and subregion-based models, are introduced to develop the guidance scheme and to test its effect, respectively. Notably, the challenge here is to translate certain variables across the traffic models without a loss of significance and assure certain degree of consistency. Chapter 4 extracts and reconstructs aggregated route choice patterns through an extensive GPS data set from taxis in a mega city. Observed GPS trajectories are first grouped together to provide a physical evidence for consistent route patterns. Second, in order to investigate the consistency of equilibrium assumptions considered in Chapter 2, observed trajectories are replaced with shortest path trajectories, and aggregated route choice patterns are reconstructed. Part II introduces novel travel time prediction and variability models. Travel time is a crucial performance measure in assessing the efficiency of transportation systems, and it provides a common index for both practitioners and travelers. Chapter 5 develops a travel time prediction model that jointly exploits traffic flow fundamentals and advanced data mining techniques. The prediction method detects the congestion patterns through the identification of active bottlenecks, and clusters the days with similar traffic patterns. This approach basically allows the model to train its predictions with relevant historical data sets. The method is applicable in oversaturated conditions and consistent with physics of traffic flow. Nevertheless, travelers not only consider travel time on average, but also value its variation. Day-to-day travel time variability, addressing the travel time variations of vehicles crossing the same route at the same period of time on different days, reveals interesting patterns. Departure time periods with similar mean travel times in the onset and offset of congestion exhibit quite different variance values. This phenomenon causes counter-clockwise hysteresis loops on the mean-variance curves. Chapter 6 investigates the empirical implications of hysteresis shape within the context of day-to-day travel time variability.

EPFL2015

Optimization-based Control, Estimation, and Identification of Urban Road Transport Systems

Isik Ilber Sirmatel

Urbanization intensifies as a global trend, exposing transportation networks to ever increasing levels of congestion. As network usage increases with available infrastructure, building new roads is not a solution. Design of intelligent transportation systems, involving identification, estimation, and feedback control methods with dynamical traffic models, is emerging as a feasible way to improve operation of existing infrastructure. Nevertheless, complexity of large-scale networks, spatiotemporal propagation of congestion, and uncertainty in traveler choices present considerable challenges for modeling, estimation, and control of road transport systems. This dissertation focuses on development of novel and practicable optimization-based traffic control and estimation methods for improving mobility in large-scale urban road networks. Part 1 is dedicated to identification, estimation, and control methods based on macroscopic traffic dynamics for perimeter controlled urban networks. Obtaining accurate estimates of model parameters and traffic states is critical for feedback perimeter control systems. In chapter 2, a nonlinear moving horizon estimation (MHE) scheme is proposed for combined state and demand estimation for a two-region urban network with dynamical modeling via macroscopic fundamental diagram (MFD). A traffic control framework consisting of identification, state estimation, and control methods is developed in chapter 3, enabling model-based feedback perimeter control of city-scale traffic. Part 2 focuses on traffic management methods considering regional route guidance. Equipping traffic controllers with route guidance carries potential for high performance congestion management. Chapter 4 contains model predictive control (MPC) schemes integrating route guidance and perimeter control actuators, capable of superior performance compared to using only perimeter control. A hierarchical traffic controller is designed in chapter 5, employing a path assignment mechanism to realize macroscopic route guidance commands of a network-level MPC.

EPFL2020