Transportmetrica A-Transport Science最新文献

With the advent of big data era, activity-based model (ABM) has once again become hot topics in the traffic planning. Traffic big data can reflect individual travel patterns, making it possible to establish ABMs. However, current ABMs based on big data are not mature, especially in the individual trip forecasting. Therefore, this paper proposes an advanced ABM using Long Short-Term Memory (LSTM) networks and mobile phone signalling data. The model is skeleton scheduling which contains primary activity chaining and secondary activity nesting. Then a time-dynamic adjustment model is proposed to adjust time conflicts among consecutive activities. A field test is conducted in Chengdu. The KS values of work and leisure departure time reach 35.20 × 10⁻² and 41.02 × 10⁻² separately, and that for activity duration reach 44.91 × 10⁻² and 54.65 × 10⁻². The results show our model can effectively predict activities, and has better accuracy and stability than existing BN, DT, GRNN, RF and GRU.

As an emerging and innovative public transportation mode, the customised bus (CB) has drawn widespread attention. Existing studies of the CB mainly focus on issues regarding the bus station location, route design, timetabling, and fare setting, but rarely consider the passenger to bus station assignment problem which can significantly influence passengers’ benefit and the buses’ operating scheme. Thus, this study focuses on the customised bus routing problem with passenger-to-station assignment (CBRP-PSA) aiming to simultaneously minimise passengers’ CB service access cost and the bus route cost. A time-discretized multi-commodity network flow model is developed to jointly determine the CB routes, timetables, and passenger-to-station assignment by allowing split loads and mixed loads. Through the dualization, the developed model is decomposed into two solvable sub-problems, and a Lagrangian-based heuristic solution algorithm is proposed. The model and algorithm are implemented in illustrative, medium-scale, and large-scale transportation networks to demonstrate their effectiveness under different scenarios.

Shared autonomous vehicles (SAVs) are a fleet of autonomous taxis that provide point-to-point transportation services for travellers, and have the potential to reshape the nature of the transportation market in terms of operational costs, environmental outcomes, increased tolling efficiency, etc. However, the number of waiting passengers could become arbitrarily large when the fleet size is too small for travel demand, which could cause an unstable network. An unstable network will make passengers impatient and some people will choose some other alternative travel modes, such as metro or bus. To achieve stable and reliable SAV services, this study designs a dynamic queueing model for waiting passengers and provides a fast maximum stability dispatch policy for SAVs when the average number of waiting for passengers is bounded in expectation, which is analytically proven by the Lyapunov drift techniques. After that, we expand the stability proof to a more realistic scenario accounting for the existence of exiting passengers. Unlike previous work, this study considers exiting passengers in stability analyses for the first time. Moreover, the maximum stability of the network doesn't require a planning horizon based on the proposed dispatch policy. The simulation results show that the proposed dispatch policy can ensure the waiting queues and the number of exiting passengers remain bound in several experimental settings.

The choice of shipment size is a vital decision in logistics and has a strong indirect influence on freight transport demand, via the choice of mode and truck type choice. Through time, shipment sizes can change as a result of new decisions in the logistics process or due to conditions external to the supply chain. This study investigates the temporal stability of shipment size choices, relating these to the choice of truck types. It uses repeated cross-sectional data for the years 2015, 2017, and 2019 collected from cordon and business establishment surveys in Addis Ababa city, Ethiopia. The integrated choice and latent variables (ICLV) and latent growth (LG) models were used to assess the time-dependent patterns of choosing shipment sizes, both at the level of the entire freight system as well as the specific truck types. The model results reveal that shipment size decisions are temporally unstable where, in our case, shipment sizes exhibited a declining trend.