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Exploring Potential Applications of Quantum Computing in Transportation Modelling
بررسی کاربردهای بالقوه محاسبات کوانتومی در مدل سازی حمل و نقل-2022 The idea that quantum effects could be harnessed
to allow faster computation was first proposed by Feynman.
As of 2020 we appear to have achieved ‘quantum supremacy’,
that is, a quantum computer that performs a given task faster
than its classical counterpart. This paper examines some possibilities opened up by potential future application of quantum
computing to transportation simulation and planning. To date,
no such research was found to exist, therefore we begin with
an introduction to quantum computing for the programmers
of transport models. We discuss existing quantum computing
research relevant to transportation, finding developments in
network analysis, shortest path computation, multi-objective
routing, optimization and calibration – of which the latter three
appear to offer the greater promise in future research. Two
examples are developed in greater detail, (1) an application of
Grover’s quantum algorithm for extracting the mean, which has
general applicability towards summarizing distributions which
are expensive to compute classically, is applied to an assignment
or betweenness model - quantum speedup is elusive in the
general case but achievable when trading speed for accuracy
for limited outputs; (2) quantum optimization is applied to an
activity-based model, giving a theoretically quadratic speedup.
Recent developments notwithstanding, implementation of quantum transportation algorithms will for the foreseeable future
remain a challenge due to space overheads imposed by the
requirement for reversible computation.
Index Terms: Quantum computing | assignment | betweenness | flows, activity models | tour models. |
مقاله انگلیسی |
2 |
Development and application of the Activity-BAsed Traveler Analyzer (ABATA) system
توسعه و کاربرد سیستم تجزیه و تحلیل مسافرتی Activity-BAsed (ABATA)-2020 While advanced technologies and big data are widely used in the transportation study, most transportation
plans still rely on some variant of traditional four-step demand forecasting models. The most
significant limitations of the four-step model are spatiotemporal aggregation of data and difficulty of
considering individual travel behaviors. To address these drawbacks, activity-based modeling systems
have increasingly been developed. In this paper, we present a new activity-based analytical system,
called Activity-BAsed Traveler Analyzer (ABATA). The distinguishing feature of ABATA is the simulation
of the present hourly service population that is determined from mobile phone data instead of a
synthetic population. ABATA comprises multiple components, including an hourly total population
estimator, activity profile constructor, hourly activity population estimator, spatial activity population
estimator, and origin–destination estimator. To demonstrate the proposed method, a future aged
society in Gangnam, Korea is evaluated as a case study. The results indicate that the hourly activity
populations engaged in work, school, and private education decreased, while those engaged in home,
shopping, recreation and other activities increased with the aging of the population. The associated
changes in mobility were found to be rational and reasonable: older people tend to have a more flexible
working time, make shorter-distance trips, undertake more trips for shopping, recreation, home, and
other activities, and finish their trips earlier, before evening. The proposed ABATA system is expected to
provide a valuable tool for simulating the impacts of future changes in population, activity schedules,
and land use on activity populations and travel demands. Keywords: Activity-based model | Mobile phone data | Hourly service population | Mobility | Aged society |
مقاله انگلیسی |