Use of Travel Demand Satisfaction to Assess Road Network Reliability

Heydecker, Benjamin; Lam, William H. K.; Zhang, Ning

doi:10.1080/18128600708685670

Cited by 36 publications

(18 citation statements)

References 14 publications

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“…Several directions for future research are suggested as follows: (1) to extend the proposed model to a multimodal transportation network in which the interaction between the auto and transit modes can be considered (Boyce 2007); (2) to consider the risk-taking behavior of passengers towards uncertainty (Bell and Cassir 2002;Szeto et al 2006;Bell 2007); (3) to assess the performance of the system using the concept of travel demand satisfaction reliability (Heydecker et al 2007), and (4) to optimize and design transit networks in which both the recurrent and non-recurrent congestion are considered.…”

Section: Discussionmentioning

confidence: 99%

The Optimal Transit Fare Structure under Different Market Regimes with Uncertainty in the Network

Lam

Wong

2008

Netw Spat Econ

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Section: Discussionmentioning

confidence: 99%

The Optimal Transit Fare Structure under Different Market Regimes with Uncertainty in the Network

Lam

Wong

2008

Netw Spat Econ

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“…In the following, we will further use f B v,s (·), the derivative of the cumulative distribution function F B v,s (·). Though Equation (1) is a suitable reliability measure (as used in previous transportation studies, such as Chen et al 2002;Heydecker, Lam, and Zhang 2007;Shariat-Mohaymany and Babaei 2010), for our problem the PDFs of the available seats and the boarding passengers must be determined in advance. Determining the PDFs for a transit line analytically is an impossible, or at least complex, task because they depend on several random variables as outlined in the previous section.…”

Section: Evaluating Reliability Of a Transit Line 31 Definition Of mentioning

confidence: 99%

The impact of irregular headways on seat availability

Babaei¹,

Schmöcker²,

Mohaymany³

2013

Transportmetrica A: Transport Science

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This paper introduces seat availability reliability, a new reliability measure that assesses the performance of transit lines. This alternative reliability indicator is defined in terms of the coefficient of variation, which examines the relative deviation of seat availability. An analytical approach is utilised based on the conditional expectation to achieve relationships for the mean and variance of the number of boarding and alighting passengers and seat availability at stops. Furthermore, confidence intervals for seat availability are derived. It is proposed that such information might be useful for traveller information systems. The proposed model is applied to a schematic transit line to show how sensitive seat availability is to headway variation (irregular dispatching) and demand uncertainty.

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“…Capacity reliability is defined as the probability that the transportation system can accommodate a given demand level at an acceptable level of service, while taking the route choice behavior into account (Chen et al, 1999, 2002b). Travel demand satisfaction reliability is concerned with the probability that the ratio of the equilibrium to latent travel demand is not less than certain acceptable value (Heydecker et al, 2007). Other than connectivity reliability, other reliability definitions mentioned earlier consider the travel choice behavior of travelers.…”

Section: Problem Formulationmentioning

confidence: 99%

“… modeling decision making of travelers under uncertainty (Chen et al, 2002a; Lo et al, 2006; Lam et al, 2008; Shao et al, 2008; Siu and Lo, 2008; Zhou and Chen, 2008; Connors and Sumalee, 2009), evaluating the performance of transport systems and finding critical components (e.g., Bell, 2000; Cassir et al, 2003; Szeto et al, 2006; Heydecker et al, 2007; Sumalee and Watling, 2008; Shimamoto et al, 2008), transport network planning and design (e.g., Lam and Xu, 1999; Lo and Tung, 2003; Sumalee et al, 2006; Chen et al, 2007; Li et al, 2008), and traffic management and control (e.g., Taylor, 1999, 2000; Hoogendoorn et al, 2008). …”

Section: Problem Formulationmentioning

confidence: 99%

Reliability-Based Transit Assignment for Congested Stochastic Transit Networks

Szeto

Solayappan

Jiang

2011

Computer-Aided Civil and Infrastructure Engineering

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This article proposes a nonlinear complementarity problem (NCP) formulation for the risk-aversive stochastic transit assignment problem in which in-vehicle travel time, waiting time, capacity, and the effect of congestion are considered as stochastic variables simultaneously and both their means and variances are incorporated into the formulation. A new congestion model is developed and captured in the proposed NCP formulation to account for different effects of on-board passengers and passengers waiting at stops. A reliabilitybased user equilibrium condition is also defined based on the proposed generalized concept of travel time budget referred to as effective travel cost, and is captured in the formulation. A column generation based algorithm is proposed to solve the NCP formulation. A survey was conducted to validate that the degree of risk aversion of transit passengers affects their route choices. Numerical studies were performed to demonstrate the problem and the effectiveness of the proposed algorithm. The results obtained show that underestimating the congestion effect and ignoring the risk aversion behavior can overestimate the patronage of transit service, which have profound im- * To whom correspondence should be addressed. E-mail: ceszeto@ hku.hk.plications on the profit of the operators involved and the development of transit network design models.

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Use of Travel Demand Satisfaction to Assess Road Network Reliability

Cited by 36 publications

References 14 publications

The Optimal Transit Fare Structure under Different Market Regimes with Uncertainty in the Network

The Optimal Transit Fare Structure under Different Market Regimes with Uncertainty in the Network

The impact of irregular headways on seat availability

Reliability-Based Transit Assignment for Congested Stochastic Transit Networks

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