5 research outputs found
Sustainability Analysis Framework for On-Demand Public Transit Systems
There is an increased interest from transit agencies to replace fixed-route
transit services with on-demand public transits (ODT). However, it is still
unclear when and where such a service is efficient and sustainable. To this
end, we provide a comprehensive framework for assessing the sustainability of
ODT systems from the perspective of overall efficiency, environmental
footprint, and social equity and inclusion. The proposed framework is
illustrated by applying it to the Town of Innisfil, Ontario, where an ODT
system has been implemented since 2017. It can be concluded that when there is
adequate supply and no surge pricing, crowdsourced ODTs are the most
cost-effective transit system when the demand is below 3.37 riders/km2/day.
With surge pricing applied to crowdsourced ODTs, hybrid systems become the most
cost-effective transit solution when demand ranges between 1.18 and 3.37
riders/km2/day. The use of private vehicles is more environmentally sustainable
than providing public transit service at all demand levels below 3.37
riders/km2/day. However, the electrification of the public transit fleet along
with optimized charging strategies can reduce total yearly GHG emissions by
more than 98%. Furthermore, transit systems have similar equity distributions
for waiting and in-vehicle travel times
Driver Heterogeneity in Willingness to Give Control to Conditional Automation
The driver's willingness to give (WTG) control in conditionally automated
driving is assessed in a virtual reality based driving-rig, through their
choice to give away driving control and through the extent to which automated
driving is adopted in a mixed-traffic environment. Within- and across-class
unobserved heterogeneity and locus of control variations are taken into
account. The choice of giving away control is modelled using the mixed logit
(MIXL) and mixed latent class (LCML) model. The significant latent segments of
the locus of control are developed into internalizers and externalizers by the
latent class model (LCM) based on the taste heterogeneity identified from the
MIXL model. Results suggest that drivers choose to "giveAway" control of the
vehicle when greater concentration/attentiveness is required (e.g., in the
nighttime) or when they are interested in performing a non-driving-related task
(NDRT). In addition, it is observed that internalizers demonstrate more
heterogeneity compared to externalizers in terms of WTG
Sustainability analysis framework for on-demand public transit systems
Abstract There is an increased interest from transit agencies to replace fixed-route transit services with on-demand public transits (ODT). However, it is still unclear when and where such a service is efficient and sustainable. To this end, we provide a comprehensive framework for assessing the sustainability of ODT systems from the perspective of overall efficiency, environmental footprint, and social equity and inclusion. The proposed framework is illustrated by applying it to the Town of Innisfil, Ontario, where an ODT system has been implemented since 2017. It can be concluded that when there is adequate supply and no surge pricing, crowdsourced ODTs are the most cost-effective transit system when the demand is below 3.37 riders/km2/day. With surge pricing applied to crowdsourced ODTs, hybrid systems become the most cost-effective transit solution when demand ranges between 1.18 and 3.37 riders/km2/day. The use of private vehicles is more environmentally sustainable than providing public transit service at all demand levels below 3.37 riders/km2/day. However, the electrification of the public transit fleet along with optimized charging strategies can reduce total yearly GHG emissions by more than 98%. Furthermore, transit systems have similar equity distributions for waiting and in-vehicle travel times
On-Demand Transit User Preference Analysis using Hybrid Choice Models
In light of the increasing interest to transform the fixed-route public
transit (FRT) services into on-demand transit (ODT) services, there exists a
strong need for a comprehensive evaluation of the effects of this shift on the
users. Such an analysis can help the municipalities and service providers to
design and operate more convenient, attractive, and sustainable transit
solutions. To understand the user preferences, we developed three hybrid choice
models: integrated choice and latent variable (ICLV), latent class (LC), and
latent class integrated choice and latent variable (LC-ICLV) models. We used
these models to analyze the public transit user's preferences in Belleville,
Ontario, Canada. Hybrid choice models were estimated using a rich dataset that
combined the actual level of service attributes obtained from Belleville's ODT
service and self-reported usage behaviour obtained from a revealed preference
survey of the ODT users. The latent class models divided the users into two
groups with different travel behaviour and preferences. The results showed that
the captive user's preference for ODT service was significantly affected by the
number of unassigned trips, in-vehicle time, and main travel mode before the
ODT service started. On the other hand, the non-captive user's service
preference was significantly affected by the Time Sensitivity and the Online
Service Satisfaction latent variables, as well as the performance of the ODT
service and trip purpose. This study attaches importance to improving the
reliability and performance of the ODT service and outlines directions for
reducing operational costs by updating the required fleet size and assigning
more vehicles for work-related trips