Microsimulation models incorporating both demand and supply dynamics

There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework. The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makers’ decisions and individual vehicle movements across the network. To achieve this it represents directly individual drivers’ choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the space–time trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamic

Modelling public transport accessibility with Monte Carlo stochastic simulations: A case study of Ostrava

Activity-based micro-scale simulation models for transport modelling provide better evaluations of public transport accessibility, enabling researchers to overcome the shortage of reliable real-world data. Current simulation systems face simplifications of personal behaviour, zonal patterns, non-optimisation of public transport trips (choice of the fastest option only), and do not work with real targets and their characteristics. The new TRAMsim system uses a Monte Carlo approach, which evaluates all possible public transport and walking origin-destination (O-D) trips for k-nearest stops within a given time interval, and selects appropriate variants according to the expected scenarios and parameters derived from local surveys. For the city of Ostrava, Czechia, two commuting models were compared based on simulated movements to reach (a) randomly selected large employers and (b) proportionally selected employers using an appropriate distance-decay impedance function derived from various combinations of conditions. The validation of these models confirms the relevance of the proportional gravity-based model. Multidimensional evaluation of the potential accessibility of employers elucidates issues in several localities, including a high number of transfers, high total commuting time, low variety of accessible employers and high pedestrian mode usage. The transport accessibility evaluation based on synthetic trips offers an improved understanding of local situations and helps to assess the impact of planned changes.Web of Science1124art. no. 709

Carsharing systems demand estimation and defined operations: a literature review

Efforts have been made in the last few decades to provide new urban transport alternatives. One of these is carsharing, which involves a fleet of vehicles scattered around a city for the use of a group of members. At first, part of the research effort was put into setting up real life experiments with vehicle fleets and observing the performance of major private operators. In the meantime, with the growth of this alternative and the need to better plan its deployment, researchers started to create more advanced methods to study carsharing systems’ planning issues. In this paper, we review those methods, identifying gaps and suggesting how to bridge them in the future. Based on that review we concluded that carsharing demand is difficult to model due to the fact that the availability of vehicles is intrinsically dependent on the number of trips and vice versa. Moreover, despite the existence of carsharing simulation models that offer very detailed mobility representations, no model is able to characterise accurately the supply side, thus hindering the cost-benefit assessment that is fundamental to justify investment in this transport alternative, in particular those that are being endorsed by the European Union. More complex, however, is the operation of the emerging one-way carsharing systems, where a vehicle may be dropped off at any station, which adds uncertainty as to the location where vehicles can be picked up. Several optimisation approaches have been proposed to mitigate this problem but they are always limited in scope and leave other aspects out for model simplification purposes. Some simulation models have also been developed to study the performance of this type of carsharing system, but they have not included ways of balancing the vehicle stocks

Travel Demand Growth: Research on Longer-Term Issues. The Potential Contribution of Trip Planning Systems

INTRODUCTION 1.1 The growth in demand for travel Over the 20 years hm 1965, National Travel Survey (NTS) data shows a 61% growth in total person - km of travel. More detailed analysis suggests that this is made up roughly as follows:- due to increased population 4% due to more journeys 22% due to longer journeys 35% This implies that around 60% of the growth in travel has been due to people travelling further, rather than making more journeys. It is interesting to note, too, that the same phenomenon occurs even in the most congested areas. Between 1975 and 1985, NTS shows an 11% growth in person -km by London residents, at a time when population fell by 5%. In this case, the growth is made up roughly as follows:- due to lost population -5% due to more journeys 4% due to longer journeys 12% It is of course difficult to estimate the extent to which future growth in travel will be generated by longer journeys. The NRTF, which predicts a growth in car-km of between 120% and 180% between 1985 and 2025, is not based on a procedure which enables the effects of journey making and journey length to be separated. However, it is worth noting that if the same pattern were to exist at a national level in future, the predicted growth in car travel due to longer journeys could be equivalent to between 75% and 100% of today's car travel. It seems appropriate to ask whether it is a wise use of scarce resources to provide the infrastructure and energy needed to enable people to carry out their activities further from home. (Continues...