Microsimulation models incorporating both demand and supply dynamics

Abstract

9There has been rapid growth in interest in real-time transport strategies over the last decade, ranging 10 from automated highway systems and responsive traffic signal control to incident management and driver 11 information systems. The complexity of these strategies, in terms of the spatial and temporal interactions 12 within the transport system, has led to a parallel growth in the application of traffic microsimulation models 13 for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, 14 macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a 15 difficulty that remains is the question of how the secondary impacts, specifically the effect on route and 16 departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation 17 framework. 18The paper describes a modelling approach to road network traffic, in which the emphasis is on the inte-19 grated microsimulation of individual trip-makersÕ decisions and individual vehicle movements across the 20 network. To achieve this it represents directly individual driversÕ choices and experiences as they evolve 21 from day-to-day, combined with a detailed within-day traffic simulation model of the space-time trajecto-22 ries of individual vehicles according to car-following and lane-changing rules and intersection regulations. 23 It therefore models both day-to-day and within-day variability in both demand and supply conditions, and 24 so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed 25 above. The full model specification is given, along with details of its algorithmic implementation. A number 26 of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-27 day variability; an application to the evaluation of alternative signal control policies; and the evaluation of 35Recent years have seen a massive increase in real-time advanced technological strategies 36 designed, for example, to reduce congestion, improve network efficiency, promote public trans-37 port use, decrease pollution and/or increase road safety. At the network-wide level, these include: 38 responsive, optimised traffic signal control, e.g. SCOOT (Hunt et al., 1981); congestion-based 39 road pricing (Oldridge, 1990); dynamic route guidance/information and variable message signs 40 (Emmerink and Nijkamp, 1999); congestion management strategies, e.g. freeway ramp-metering, 41 gating (Papageorgiou et al., 1989); public transport priority measures such as responsive bus sig-42 nal controls (Quinn, 1992), bus lanes and guided bus schemes (Liu et al., 1999). 43A general property of all these strategies is that they both respond to-and in turn influence-44 prevailing congestion levels, rather than being designed on the basis of long-term average condi-45 tions. That is to say, the variation in traffic conditions is just as important a consideration as t...

Model Representation & Decision-Making in an Ever-Changing World: The Role of Stochastic Process Models of Transportation Systems

“…For illustration, we shall adapt Example 4 (simplifying to no habitual effect, i.e.  = 1 in the notation of Example 4) to follow the spirit of a model presented in Liu et al (2006). We shall suppose three interrelated aspects to the micro-simulation: in the propagation of individual vehicles through the network along given routes, in the way in which individuals learn from their own (but not others experience and in the individual-specific attributes that motivate the decision to select a route.…”

Disaggregate Path Flow Estimation in an Iterated Dynamic Traffic Assignment Microsimulation

“…Simulation results can be obtained throughout the evolution and on not just the means but also variances and probability distributions both within-day and between days. The full details of the DRACULA suite of models and their applications have been reported elsewhere (e.g., Hollander and Liu, 2008;Liu et al, 2006;Liu and Tate, 2004;Panis et al, 2006) and will therefore not be detailed herein.…”

To Adapt or Not to Adapt – Consequences of Adapting Driver and Traffic Light Agents

“…Liu and colleagues [6] describe a modelling approach that integrates microsimulation of individual trip-makers' decisions and individual vehicle movements across the network. Moreover their focus is on the description of the methodology that integrates both demand and supply dynamics, so that the applications are only briefly described and not many options for the operation and control of Traffic Lights are reported.…”

“…One of them can roughly be described as greedy, while the other is fixed signal plan based. In the present paper, we do not explore the methodological issues as in [6] but, rather, investigate in more details particular issues of the integration and interaction between actors from the supply and demand side. Figure 1 shows a scheme of our approach based on the interaction between supply and demand.…”