Travel Demand Uncertainty Research Articles

A predictive chance constraint rebalancing approach to mobility-on-demand services

This paper considers the problem of supply-demand imbalances in Mobility-on-Demand (MoD) services. These imbalances occur due to uneven stochastic travel demand and can be mitigated by proactively rebalancing empty vehicles to areas where the demand is high. To achieve this, we propose a method that takes into account uncertainties of predicted travel demand while minimizing pick-up time and rebalance mileage for autonomous MoD ride-hailing. More precisely, first travel demand is predicted using Gaussian Process Regression (GPR) which provides uncertainty bounds on the prediction. We then formulate a stochastic model predictive control (MPC) for the autonomous ride-hailing service and integrate the demand predictions with uncertainty bounds. In order to guarantee constraint satisfaction in the optimization under estimated stochastic demand prediction, we employ a probabilistic constraining method with user-defined confidence interval, using Chance Constrained MPC (CCMPC). The benefits of the proposed method are twofold. First, travel demand uncertainty prediction from data can naturally be embedded into the MoD optimization framework, allowing us to keep the imbalance at each station below a certain threshold with a user-defined probability. Second, CCMPC can be relaxed into a Mixed-Integer-Linear-Program (MILP) and the MILP can be solved as a corresponding Linear-Program, which always admits an integral solution. Our transportation simulations show that by tuning the confidence bound on the chance constraint, close to optimal oracle performance can be achieved, with a median customer wait time reduction of 4% compared to using only the mean prediction of the GPR.

Robust Design of Electric Charging Infrastructure Locations under Travel Demand Uncertainty and Driving Range Heterogeneity

The rising demand for electric vehicles (EVs), motivated by their environmental benefits, is generating an increased need for EV charging infrastructure. Also, it has been recognized that the adequacy of such infrastructure helps promote EV use. Therefore, to facilitate EV adoption, governments seek guidance on continued investments in EV charging infrastructure development. Such investment decisions, which include EV charging station locations and capacities, and the timing of such investments require robust estimates of future travel demand and EV battery range constraints. This paper develops and implements a framework to establish an optimal schedule and locations for new charging stations and decommissioning gasoline refueling stations over a long-term planning horizon, considering the uncertainty in future travel demand forecasts and the driving range heterogeneity of EVs. A robust mathematical model is proposed to solve the problem by minimizing not only the worst-case total system travel cost but also the total penalty for unused capacities of charging stations. This study uses an adaptation of the cutting-plane method to solve the proposed model. Based on two key decision criteria (travelers’ cost and charging supply sufficiency), the results indicate that the robust scheme outperforms its deterministic counterpart.

Robust Design of Electric Charging Infrastructure Locations under Travel Demand Uncertainty and Driving Range Heterogeneity

Robust Design of Electric Charging Infrastructure Locations under Travel Demand Uncertainty and Driving Range Heterogeneity

Customized bus service design for uncertain commuting travel demand

This study proposes an interesting customized bus service design problem by considering travel demand uncertainty. Given a fleet of heterogeneous vehicles, a mixed integer linear programming (MILP) model is put forward for the complex decision making on bus routing, timetabling and bus deployment, with the objective of generating a set of profitable bus services to cater for diverse commuting-trip requests. To capture the risk-averse level of the bus operator in uncertain travel demand environment, a random variable describing the likelihood that the offered bus services are rejected by potential passengers and two associated control parameters are embedded in the MILP model, facilitating an adjustable robust optimization framework. A branch-and-price method is implemented to solve the model exactly. A column-generation-based heuristic method is proposed to solve large-scale problems. The effectiveness of both the exact and heuristic methods is assessed in numerical experiments.

Strategic dynamic traffic assignment incorporating travel demand uncertainty

ABSTRACTDynamic traffic assignment (DTA) research has advanced greatly in terms of deployability, computational feasibility, and representing complex temporal phenomena. There have also been substantial contributions regarding various aspects of stochasticity within DTA. However, there are persisting limitations in terms of approaches which are both computationally tractable and provide more detailed representation of stochastic aspects. This paper explores the application of a novel Strategic User Equilibrium DTA (StrDTA) modelling framework, which captures the impact of users making a priori route choice decisions based on the knowledge of a range of possible travel demand scenarios (e.g. differing days or representative situations). The resulting stochastic DTA problem becomes complex due to the integration of multiple demand scenarios and the algorithmic adjustments necessary to find optimal paths. A new solution framework is proposed which still permits implementation, and a detailed case study is presented for the Sydney Central Business District network. Results demonstrate the importance of accounting for stochasticity in the routing algorithm rather than relying on assumptions of average values.

A robust signal control system for equilibrium flow under uncertain travel demand and traffic delay

Abstract A robust signal control system (AROSS) for equilibrium flow is presented for urban road networks under uncertain travel demand and traffic delay. The optimal solutions for AROSS can be formulated as a smoothing mathematical program with equilibrium constraints (SMPEC) and efficiently solved by a projected bundle set method with global convergence. While conventional robust optimization is considered a good alternative against high-consequence realization of uncertainty, a traffic responsive signal control using a hybrid strategy of scenarios is considered. Sensitivity analysis of SMPEC with respect to signal settings against randomized budget uncertainty in travel demand and link delay is performed. Numerical comparisons are also made with other heuristics using real-data road networks. Results indicate that AROSS enjoyed a greater gain of achieving road network robustness while effectively attenuating sub-optimality of robust optimum as compared to recent alternatives.

Robust Stochastic Design of Signal-Controlled Road Network Under Uncertain Travel Demands

A robust stochastic design and signal control (RoSign) is considered for urban traffic network under uncertain travel demand. The optimal solutions for RoSign can be cast as a stochastic mathematical program with equilibrium constraints (SMPEC) and efficiently solved by a stochastic projected cutting plane (SPC) with global convergence. While robust optimization is considered a good alternative against high-consequence realization of uncertainty, a flexible signal timing using a budget of uncertainty in demand is investigated. Sensitivity analysis of SPC against budgeted uncertainty in travel demand is performed. Numerical computation and comparison are also made with other heuristics using real-data road networks. Results indicate that SPC enjoyed a greater gain of achieving road network robustness whilst incurring less sub-optimality loss against uncertain demand as compared to other alternatives.

A risk-averse signal setting policy for regulating hazardous material transportation under uncertain travel demand

To effectively regulate risk associated with hazmat transportation and minimize travel cost over road network under uncertain travel demand, a min-max bi-level programming problem (MMBLPP) is proposed to determine traffic signal settings. A risk-averse signal setting policy using budget of uncertainty (BOU) is presented against the worst-case realization of uncertainty in travel demand. A risk-averse model determining system optimum routes for hazmat carriers is presented where travel delay incurred by regular traffic at downstream junctions has been taken into account. A min-max single-level model (MMX) and a bundle-like solution method are proposed to determine risk-averse signal settings under BOU-based travel demand. To investigate the effectiveness of proposed model, numerical evaluations using real-world road network are empirically made with various scenarios of transport policies. These results reported obviously indicate that the proposed model achieved relatively better results than did traditional one against high-consequence scenarios of uncertain travel demand while substantially mitigating risk in the worst cases.

A bi-level decision support system for uncertain network design with equilibrium flow

A bi-level decision support system (BDSS) is proposed for a normative road network design with uncertain travel demand. A bi-level decision support model with link capacity expansion is developed to simultaneously reduce travel delay to road users and mitigate vulnerability of road network. A tractable solution scheme for BDSS is developed. Due to some hierarchy in decision-making order of BDSS, a bi-level programming is employed. A risk-averse Stackelberg solution is established for a normative BDSS under travel demand uncertainty. Numerical computations are performed using a real-data road network. Computational results indicate that the proposed solution scheme can effectively improve a worst-case performance of BDSS with greater success while incurring a relatively slighter loss of optimality when compared to deterministic solutions at nominal condition. Particularly, our computation results showed that proposed solution becomes more attractive as the realization taken by unknown demand growth factor increases.

Robust Speed Limits Scheme Design for Bimodal Transportation Systems

In the context of travel demand uncertainty, this paper investigates how to determine the robust road speed limits for improving mobility and lowering vehicular emissions in bimodal transportation systems that involve private cars and subway. More specifically, the total demand vector is supposed to vary within a given set. Our target is to find the optimal road speed limits against the worst feasible demand scenario so as to minimize the sum social cost of system travel time and vehicular emissions. In order to estimate traffic emissions more reliably, motor vehicle emission simulator (MOVES) is utilized to simulate the emission factor function with respect to average speed. On these bases, we formulate the robust speed limits design problem as a “min-max” nonlinear model with complementarity constraints and solve it iteratively by a cutting-plane scheme that contains two sub-MPCCs. A numerical example is illustrated at the end.

A framework for evaluating the role of electric vehicles in transportation network infrastructure under travel demand variability

The introduction of plug-in electric vehicles (PEVs) represents an unprecedented interaction between the road network and electricity grid. By replacing the traditional fuel source, petrol, with electricity, PEVs will increase the demand for electric power in a region and change emission profiles. Overall, the impacts depend on the eventual penetration of PEV ownership, but the true market share of PEVs in the future is highly unclear and radically different scenarios are possible. This added forecasting volatility makes long-term transport models that explicitly consider travel demand uncertainty even more critical. This work utilizes transport modeling tools in order to quantify the relationship between the travel patterns of PEV drivers and PEV energy consumption rates, as well as the corresponding environmental impact (measured by emissions savings relative to traditional internal combustion engine vehicles). Furthermore, this research explicitly addresses the relationship between long term travel demand uncertainty and system level energy consumption variability, an essential issue for regional energy providers and planners. Results and implications are discussed on both a small demonstration network and the Sioux Falls network.

Reliability-Based Marginal Cost Pricing Problem Case with Both Demand Uncertainty and Travelers’ Perception Errors

Focusing on the first-best marginal cost pricing (MCP) in a stochastic network with both travel demand uncertainty and stochastic perception errors within the travelers’ route choice decision processes, this paper develops a perceived risk-based stochastic network marginal cost pricing (PRSN-MCP) model. Numerical examples based on an integrated method combining the moment analysis approach, the fitting distribution method, and the reliability measures are also provided to demonstrate the importance and properties of the proposed model. The main finding is that ignoring the effect of travel time reliability and travelers’ perception errors may significantly reduce the performance of the first-best MCP tolls, especially under high travelers’ confidence and network congestion levels. The analysis result could also enhance our understanding of (1) the effect of stochastic perception error (SPE) on the perceived travel time distribution and the components of road toll; (2) the effect of road toll on the actual travel time distribution and its reliability measures; (3) the effect of road toll on the total network travel time distribution and its statistics; and (4) the effect of travel demand level and the value of reliability (VoR) level on the components of road toll.

Stochastic Network Design Problem with Fuzzy Goals

The transportation network design problem (NDP) is a high capital investment decision-making problem that inherently involves both subjective and objective uncertainties as well as multiple objectives. Goal programming is a practically useful approach with an explicit consideration of planners’ goal setting and priority structure among the multiple objectives. This paper describes the development of a hybrid goal programming (HGP) approach for modeling both subjective and objective uncertainties simultaneously in the NDP decision-making process. Planners’ subjective uncertainty regarding the linguistic setting of goals and priority structure is characterized as a set of fuzzy variables with nonlinear achievement and satisfaction functions, and the objective travel demand uncertainty is characterized as a set of random variables with predefined probability distributions. The HGP-NDP is formulated as a chance-constrained model in a bi-level programming framework and solved by a genetic algorithm procedure based on random simulation and fuzzy evaluation. The paper provides numerical examples and a real case study to demonstrate the features and applicability of the proposed HGP approach in solving the NDP under an uncertain environment.

Uncertainty in travel demand forecasting models: literature review and research agenda

Reasoning why uncertainty analysis will become important in years to come, this paper reviews prior work on uncertainty analysis in travel demand forecasting. Different sources of uncertainty are discussed. Studies examining these various sources of uncertainty are summarized differentiating between four step models, discrete choice models and activity-based models of travel demand. Next, gaps in the literature and avenues of future research are systematically discussed with a special focus on complex activity-based models. The paper is completed with some concluding comments.

Influence of Demand Uncertainty and Correlations on Traffic Predictions and Decisions

Abstract: Decisions to improve a regional transportation network are often based on predictions of future link flows that assume future travel demand is a deterministic matrix. Despite broad awareness of the uncertainties inherent in forecasts, rarely are uncertainties considered explicitly within the methodological framework due at least in part to a lack of knowledge as to how uncertainties affect the optimality of decisions. This article seeks to address this issue by presenting a new method for evaluating future travel demand uncertainty and finding an efficient technique for generating multiple realizations of demand. The proposed method employs Hypersphere Decomposition, Cholesky Decomposition, and user equilibrium traffic assignment. Numerical results suggest that neglecting correlations between the future demands of travel zone pairs can lead to improvement decisions that are less robust and could frequently rank improvements improperly. Of the six sampling techniques employed, Antithetic sampling generated travel demand realizations with the least relative bias and error.

Robust improvement schemes for road networks under demand uncertainty

Abstract This paper is concerned with development of improvement schemes for road networks under future travel demand uncertainty. Three optimization models, sensitivity-based, scenario-based and min–max, are proposed for determining robust optimal improvement schemes that make system performance insensitive to realizations of uncertain demands or allow the system to perform better against the worst-case demand scenario. Numerical examples and simulation tests are presented to demonstrate and validate the proposed models.

ANALYSIS OF POLICY AND REGULATION ON BUILD-OPERATETRANSFER SCHEME: A CASE STUDY OF THE BAN PONGKANCHANABURI MOTORWAY IN THAILAND

The Build-Operate-Transfer (BOT) network design problem often involves three parties: the government, private sectors, and road users. Each of the parties has different objectives that often conflict with each other. This study develops the optimal pricing model for each party's perspective under travel demand uncertainty. Numerical studies are provided to illustrate the tradeoff among different objectives and the effects of regulation and policy on the BOT project. Regulation and policy are normally imposed by the government to ensure the BOT project satisfy certain requirements. The impact of imposing regulation and policy on profit, social welfare, spatial equity, and financial performance of a BOT project are explored using a case study of the Ban Pong-Kanchanaburi Motorway (BKM) in Thailand.

ECONOMIC ANALYSIS IN THE MAINTENANCE, REPAIR, AND OVERHAUL INDUSTRY: AN OPTIONS APPROACH

This paper presents a practical case application of the real options framework to a multi-stage investment in the aerospace maintenance, repair, and overhaul (MRO) industry. With the deregulated commercial airline industry and military procurement reform, the $44 billion MRO sector must implement tools to remain strategically poised. In this case study, the MRO participant values a multi-stage irreversible expenditure in maintenance equipment and processes under air travel demand uncertainty using a real options analysis. The investment scenario is viewed from a delay, growth, and compound options framework, and an appropriate sensitivity analysis is performed. The main contribution of this paper is the detailed real option framing, valuation, and discussion that may be used as an illustration for industry practitioners or classroom instruction.