Sebastián Raveau

Sequential and Simultaneous Estimation of Hybrid Discrete Choice Models: Some New Findings

The formulation of hybrid discrete choice models, including both observable alternative attributes and latent variables associated with attitudes and perceptions, has become a topic of discussion once more. To estimate models integrating both kinds of variables, two methods have been proposed: the sequential approach, in which the latent variables are built before their integration with the traditional explanatory variables in the choice model and the simultaneous approach, in which both processes are done together, albeit with a sophisticated but fairly complex treatment. Here both approaches are applied to estimate hybrid choice models by using two data sets: one from the Santiago Panel (an urban mode choice context with many alternatives) and another consisting of synthetic data. Differences between both approaches were found as well as similarities not found in earlier studies. Even when both approaches result in unbiased estimators, problems arise when valuations are obtained such as the value of time for forecasting and policy evaluation.

Microsimulation of Demand and Supply of Autonomous Mobility On Demand

Agent-based models have gained wide acceptance in transportation planning because with increasing computational power, large-scale people-centric mobility simulations are possible. Several modeling efforts have been reported in the literature on the demand side (with sophisticated activity-based models that focus on an individual’s day activity patterns) and on the supply side (with detailed representation of network dynamics through simulation-based dynamic traffic assignment models). This paper proposes an extension to a state-of-the-art integrated agent-based demand and supply model—SimMobility—for the design and evaluation of autonomous vehicle systems. SimMobility integrates various mobility-sensitive behavioral models in a multiple time-scale structure comprising three simulation levels: (a) a long-term level that captures land use and economic activity, with special emphasis on accessibility; (b) a midterm level that handles agents’ activities and travel patterns; and (c) a short-term level that simulates movement of agents, operational systems, and decisions at a microscopic granularity. In that context, this paper proposes several extensions at the short-term and midterm levels to model and simulate autonomous vehicle systems and their effects on travel behavior. To showcase these features, the first-cut results of a hypothetical on-demand service with autonomous vehicles in a car-restricted zone of Singapore are presented. SimMobility was successfully used in an integrated manner to test and assess the performance of different autonomous vehicle fleet sizes and parking station configurations and to uncover changes in individual mobility patterns, specifically in regard to modal shares, routes, and destinations.

Use of Mixed Stated and Revealed Preference Data for Crowding Valuation on Public Transport in Santiago, Chile

The valuation of comfort on public transport is presented with mixed stated preference and revealed preference data. In this case, comfort is measured mainly as the level of crowding in the vehicles (bus or train) with the use of in-vehicle passenger density (in number of passengers per square meter). The data used to value comfort include a stated preference survey in which crowding levels are presented as illustrations and revealed preference data on route choice on the subway network of Santiago, Chile. The survey data are used to estimate discrete choice models and obtain a subjective valuation of passenger density through the parameters of the utility function. Disutility for traveling in crowding conditions is assumed to be proportional to the travel time; therefore, the longer the trip, the higher the utility loss. Results indicate that passenger density has a significant effect on the utility of public transportation modes. In fact, marginal disutility of travel time in a crowded vehicle (6 passengers/m2) is twice the marginal disutility in a vehicle with a low level of crowding (1 passenger/m2).

Smartphone-Based Survey for Real-Time and Retrospective Happiness Related to Travel and Activities

Understanding and incorporating measures of travel and activity well-being in transportation research are critical for the design and evaluation of policies. In recent years, several efforts have been made to quantify travelers’ subjective well-being by using a self-reported state of happiness during participation in various activities or travel patterns. The inadequacies of these conventional survey methods in collecting uninterrupted and comprehensive information have restricted the number of such studies. In this study, a smartphone-based sensing platform was adapted to collect mobility information and measure happiness. Two surveys were conducted with respondents from five continents. Real-time and retrospective happiness measures are compared and explained. Results indicate that different cognitive biases affect the levels of happiness reported by the individuals. In comparison with staying at home, performing work and education activities tends to result in lower levels of happiness, while performing other activities tends to result in higher levels of happiness. Activity duration has a significant effect on real-time happiness but is less significant for retrospective happiness.

Evaluating Off-Peak Pricing Strategies in Public Transportation with an Activity-Based Approach

Public transportation authorities across the world are implementing various peak and off-peak pricing strategies to manage travel demand and improve the overall system performance. In this study, an activity-based demand framework was used to evaluate two off-peak pricing strategies currently in use in Singapore. These strategies consisted of a free prepeak travel on mass rapid transit (MRT) and an off-peak discount for an integrated transit (public buses and MRT). Smart card data collected before and after the implementation of the first policy were used to calibrate the behavioral models involved, to capture travelers’ preferences and choices properly. To evaluate both pricing strategies, a comprehensive set of key performance indicators was considered and included the changes in peak ridership, average trip fare, operator’s revenue, the number of public transportation trips, and mode share. The results indicate that off-peak discount pricing strategies are a viable policy option for spreading demand peaks and that they are more effective during the afternoon peak period. This study also demonstrates the capabilities and the advantages of an agent-based modeling platform, SimMobility, as a tool for policy analysis.

Numerical Bounds on the Price of Anarchy

Theoretical upper bounds for price of anarchy have been calculated in previous studies. We present an empirical analysis for the price of anarchy for congested transportation networks; different network sizes and demand levels are considered for each network. We obtain a maximum price of anarchy for the cases studied, which is notably lower than the theoretical bounds reported in the literature. This result should be carefully considered in the design and implementation of road pricing mechanisms for cities.

From Transit Systems to Models: Purpose of Modelling

From Part I of the book, it will be obvious that public transport plays an essential role in providing mobility to people, especially in dense urban areas. The social welfare generated by good public transport comes at a price, however. Almost all forms require large investments into infrastructure, vehicles and operation. With limited finance, ideal public transport remains a distant goal, and a lot of effort goes into finding an optimal allocation of budget to investment options. The key question for these decisions is: How big is the total benefit of a proposed investment? To answer it, one needs to predict how the potential users will make use of the hypothetical improved public transport. For responsible decision-making, this prediction should be rational, transparent and accountable. It is no surprise therefore that models are typically used to produce the predictions. These models span the whole range of mobility decisions made by individuals, from long term to short term. Passenger route choice, the focus of Part III, accounts for only a part of the complex decision hierarchy. Before zooming into route choice models, this chapter looks at the planning process as a whole, explains the role of models in decision-making and gives an overview of the whole decision hierarchy. The last two sections introduce the general mathematical framework, in which decision models are formulated and set the stage for the description of specific models.

A Maximum Entropy Fixed-Point Route Choice Model for Route Correlation

In this paper we present a stochastic route choice model for transit networks that explicitly addresses route correlation due to overlapping alternatives. The model is based on a multi-objective mathematical programming problem, the optimality conditions of which generate an extension to the Multinomial Logit models. The proposed model considers a fixed point problem for treating correlations between routes, which can be solved iteratively. We estimated the new model on the Santiago (Chile) Metro network and compared the results with other route choice models that can be found in the literature. The new model has better explanatory and predictive power that many other alternative models, correctly capturing the correlation factor. Our methodology can be extended to private transport networks.