A utility-based dynamic demand estimation model that explicitly accounts for activity scheduling and duration

Abstract This paper proposes a Dynamic Demand Estimation (DODE) framework that explicitly accounts for activity scheduling and duration. By assuming a Utility-Based departure time choice model, the time-dependent OD flow becomes a function, whose parameters are those of the utility function(s) within the departure time choice model. In this way, the DODE is solved using a parametric approach, which, on one hand, has less variables to calibrate with respect to the classical bi-level formulation while, on the other hand, it accounts for different trip purposes. Properties of the model are analytically and numerically discussed, showing that the model is more suited for estimating the systematic component of the demand with respect to the standard GLS formulation. Differently from similar approaches in literature, which rely on agent-based microsimulators and require expensive survey data, the proposed framework is applicable with all those DTA models, which are based on OD matrix, and do not necessarily need any data at user level. This has been proven by applying the proposed approach with a standard macroscopic realistic Dynamic Traffic Assignment (DTA).

[1]  Kenneth A. Small,et al.  THE SCHEDULING OF CONSUMER ACTIVITIES: WORK TRIPS , 1982 .

[2]  Ennio Cascetta,et al.  Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts , 1993, Transp. Sci..

[3]  Satoshi Fujii,et al.  Analysis of Time Allocation, Departure Time, and Route Choice Behavior Under Congestion Pricing , 2000 .

[4]  Michael J. Maher,et al.  A bi-level programming approach for trip matrix estimation and traffic control problems with stochastic user equilibrium link flows , 2001 .

[5]  Hani S. Mahmassani,et al.  Dynamic User Equilibrium Departure Time and Route Choice on Idealized Traffic Arterials , 1984, Transp. Sci..

[6]  Dirk Cattrysse,et al.  A generic class of first order node models for dynamic macroscopic simulation of traffic flows , 2011 .

[7]  K. Nagel,et al.  Behavioral Calibration and Analysis of a Large-Scale Travel Microsimulation , 2012 .

[8]  Hani S. Mahmassani,et al.  Activity-Based Model with Dynamic Traffic Assignment and Consideration of Heterogeneous User Preferences and Reliability Valuation , 2015 .

[9]  Hjp Harry Timmermans,et al.  Modeling Departure Time Choice in the Context of Activity Scheduling Behavior , 2003 .

[10]  E. Cascetta Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator , 1984 .

[11]  Fulvio Simonelli,et al.  A network sensor location procedure accounting for o–d matrix estimate variability , 2012 .

[12]  Satish V. Ukkusuri,et al.  B‐Dynamic: An Efficient Algorithm for Dynamic User Equilibrium Assignment in Activity‐Travel Networks 1 , 2011, Comput. Aided Civ. Infrastructure Eng..

[13]  Ernesto Cipriani,et al.  An Adaptive Bi-Level Gradient Procedure for the Estimation of Dynamic Traffic Demand , 2014, IEEE Transactions on Intelligent Transportation Systems.

[14]  Moshe Ben-Akiva,et al.  W-SPSA in Practice: Approximation of Weight Matrices and Calibration of Traffic Simulation Models , 2015 .

[15]  D. Ettema,et al.  Modelling the joint choice of activity timing and duration , 2007 .

[16]  Xiaoning Zhang,et al.  Integrated scheduling of daily work activities and morning–evening commutes with bottleneck congestion , 2005 .

[17]  Francesco Viti,et al.  Sensor Locations for Reliable Travel Time Prediction and Dynamic Management of Traffic Networks , 2008 .

[18]  Francesco Viti,et al.  New Gradient Approximation Method for Dynamic Origin–Destination Matrix Estimation on Congested Networks , 2011 .

[19]  Kenneth A. Small,et al.  The bottleneck model: An assessment and interpretation , 2015 .

[20]  Hai Yang,et al.  An analysis of the reliability of an origin-destination trip matrix estimated from traffic counts , 1991 .

[21]  C.D.R. Lindveld Dynamic O-D Matrix Estimation: A Behavioural Approach , 2003 .

[22]  Fulvio Simonelli,et al.  Limits and perspectives of effective O-D matrix correction using traffic counts , 2009 .

[23]  F. Viti,et al.  Dynamic origin–destination estimation in congested networks: theoretical findings and implications in practice , 2013 .

[24]  W. Vickrey Congestion Theory and Transport Investment , 1969 .

[25]  Ruben Corthout,et al.  An efficient iterative link transmission model , 2016 .

[26]  Muhammad Adnan Linking Macro-level Dynamic Network Loading Models with Scheduling of Individual's Daily Activity- Travel Pattern , 2010 .

[27]  Sze Chun Wong,et al.  Bottleneck model revisited: An activity-based perspective , 2014 .

[28]  A. Palma,et al.  Economics of a bottleneck , 1986 .

[29]  William H. K. Lam,et al.  A network equilibrium approach for modelling activity-travel pattern scheduling problems in multi-modal transit networks with uncertainty , 2013, Transportation.

[30]  J. Kiefer,et al.  Stochastic Estimation of the Maximum of a Regression Function , 1952 .

[31]  Ernesto Cipriani,et al.  Towards a generic benchmarking platform for origin–destination flows estimation/updating algorithms: Design, demonstration and validation , 2016 .

[32]  E. Jenelius,et al.  c-SPSA: Cluster-wise simultaneous perturbation stochastic approximation algorithm and its application to dynamic origin–destination matrix estimation , 2015 .

[33]  Hai Yang,et al.  Optimal traffic counting locations for origin–destination matrix estimation , 1998 .

[34]  Fulvio Simonelli,et al.  Quasi-dynamic estimation of o–d flows from traffic counts: Formulation, statistical validation and performance analysis on real data , 2013 .

[35]  Ernesto Cipriani,et al.  Effectiveness of link and path information on simultaneous adjustment of dynamic O-D demand matrix , 2014 .