This chapter discusses the need for the next generation of travel demand models. Tracing
the origins of the IATBR from its first conference, many advances in travel demand modeling …

The emergence of ride-hailing, technology-enabled on-demand services such as Uber and
Lyft, has arguably impacted the daily travel behavior of users. This study analyzes the travel …

The rapid growth of internet based ride-sharing brings great changes to residents' travel and
city traffic. However, few studies had employed empirical data to examine the unique travel …

Ride-pooling has attracted considerable attention from both academia and practitioners in
recent years, promising to reduce traffic volumes and its negative impacts in urban areas …

The rapidly-growing business of ride-on-demand (RoD) service such as Uber, Lyft and Didi
proves the effectiveness of their new service model–using mobile apps and dynamic pricing …

In this study, we use existing modeling tools and data from the San Francisco Bay Area
(California) to understand the potential market demand for a “first” mile transit access service …

By serving two or more passenger requests in each ride in ride-sourcing markets, ride-
pooling service is now becoming an important component of shared smart mobility. It is …

On-demand ride-hailing platforms like Uber and Lyft are helping reshape urban
transportation, by enabling car owners to become drivers for hire with minimal overhead …

Global positioning system (GPS) data generated from taxi trips is a valuable source of
information that offers an insight into travel behaviours of urban populations with high spatio …

Traditional methods of travel data collection are often limited by high costs, infrequent
updates, or small sample sizes. Several emerging technologies, such as mobile phone …