Travel Behavior

Hypernetworks and Supply-Demand Equilibrium Obtained with Disaggregate Demand Models

Sheffi, Yosef
Carlos Daganzo
1978

This paper presents a framework for discussing many transportation demand and supply-demand equilibrium problems. It regards the sequence of choices an individual faces when he or she is about to make a travel (or not-to-travel) decision as a case choice of a route on an abstract network (hypernetwork). Hypernetworks are intimately related to the multinomial probit (MNP) model of travel choice. For instance, the multivariate normal distribution underlying this model enables one to represent processes of travel choice as route choices on networks and to use the networks as visual aids...

Equilibrium Model for Carpools on an Urban Network

Carlos Daganzo
1981

Traffic equilibrium methods are presented in which the population of motorists consists of individuals who are minimizers of a linear combination of cost and travel time. The relative importance of travel time versus cost varies across the population, but fairly mild conditions for the existence and uniqueness of the equilibrium can nevertheless be identified. The paradigm is of particular interest for carpooling studies because the occupants of carpools can divide the cost among themselves but they cannot do the same with the travel time. Thus, vehicles that have different...

The Length of Tours in Zones of Different Shapes

Carlos Daganzo
1982
The object of this paper is to explain how the expected length of traveling salesman tours changes with zone shape. To do this, a simple strategy that yields good traveling salesman tours is presented. The resulting tours are suboptimal but appear to be close to those that can be obtained by hand. Thus, the formulas that are provided may also be indicative of the length of tours built with better strategies. The results of this paper are useful for the design of distribution systems.

Extrapolating Automobile Usage Data to Long Time Periods

Horowitz, Abraham D.
Carlos Daganzo
1986

This study illustrates a statistical procedure that can be used to estimate the fraction of a given population experiencing a “rare” event during a long time period, given a few days of observation. In an automobile usage context, the rare event could be the occurrence of an automobile occupancy of four or more persons and/or a travel distance of 100 miles or more on any given day. The technique, which can be important for the design of durable goods, is illustrated with four numerical examples.

Predictability of Time-Dependent Traffic Backups and Other Reproducible Traits in Experimental Highway Data

Smilowitz, Karen
Carlos Daganzo
1999

Traffic data from a 4-mile long congested rural road in Orinda, California, are used to show that traffic delays and vehicle accumulations between any two generic observers located inside a road section can be predicted from the traffic counts measured at the extremes of the section. The traffic model does not require "recalibration" on the day of the experiment, and works well despite what appears to be location-specific driver behavior.

Multi-Lane Hybrid Traffic Flow Model: Quantifying the Impacts of Lane-Changing Maneuvers on Traffic Flow

Laval, Jorge A.
Carlos Daganzo
2004

A multi-lane traffic flow model realistically captures the disruptive effects of lane- changing vehicles by recognizing their limited ability to accelerate. While they accelerate, these vehicles create voids in the traffic stream that affect its character. Bounded acceleration explains two features of freeway traffic streams: the capacity drop of freeway bottlenecks, and the quantitative relation between the discharge rate of moving bottlenecks and bottleneck speed. The model com- bines a multilane kinematic wave module for the traffic stream, with a detailed constrained-motion model to...

Clockwise Hysteresis Loops in the Macroscopic Fundamental Diagram

Gayah, Vikash V.
Carlos Daganzo
2010

A recent study reported that the Macroscopic Fundamental Diagram of a medium size city exhibited a clockwise hysteresis loop on a day in which a major disturbance caused many drivers to switch to unfamiliar routes. This paper shows that clockwise loops are to be expected when there are disturbances, especially if the disturbances cause a significant fraction of the drivers to not change routes adaptively. It is shown that when drivers are not adaptive networks are inherently more unstable as they recover from congestion than as they are loaded. In other words, during recovery congestion...

Jitney-Lite: A Flexible-Route Feeder Service for Developing Countries

Sangveraphunsiri, Tawit
Michael Cassidy
Carlos Daganzo
2022

The paper develops a novel strategy for delivering feeder service in support of trunk-line transit. The strategy is well suited to developing countries, where costs of emergent communication technologies often preclude their use. The strategy, termed Jitney-lite, is a form of collective transportation that provides a degree of flexibility. Patrons who board an outbound Jitney-lite vehicle at a transit station are delivered to their doorsteps. On the return trip to the station, the vehicle boards new patrons in the manner of traditional, fixed-route, fixed-stop feeder-bus service. Continuum...

A Tutorial on Neural Network-Based Solvers for Hyperbolic Conservation Laws: Supervised vs. Unsupervised Learning, and Applications to Traffic Modeling

Canesse, Alexi
Fu, Zhe
Lichtle, Nathan
Matin, Hossein Nick Zinat
Liu, Zhe
Maria Laura Delle Monache
Alexandre Bayen
2025

Neural networks (NNs) are powerful tools for solving complex partial differential equations (PDEs) with high accuracy. However, many NN-based solvers are designed as general-purpose models or lack theoretical grounding, limiting their ability to capture essential solution properties such as regularity, conservation, and entropy conditions. This issue is especially critical for hyperbolic conservation laws, which govern wave propagation and shock formation, and are among the most challenging PDEs to solve accurately. This tutorial examines both supervised and unsupervised NN-based solvers...

Strategizing Equitable Transit Evacuations: A Data-driven Reinforcement Learning Approach

Tang, Fang
Wang, Han
Maria Laura Delle Monache
2025

As natural disasters become increasingly frequent, the need for efficient and equitable evacuation planning has become more critical. This paper proposes a data-driven, reinforcement learning (RL)-based framework to optimize public transit operations for bus-based evacuations in transportation networks with an emphasis on improving both efficiency and equity. We model the evacuation problem as a Markov Decision Process (MDP) solved by RL, using real-time transit data from General Transit Feed Specification (GTFS) and transportation networks extracted from OpenStreetMap (OSM). The RL agent...