Activity-based model based on long short-term memory network and mobile phone signalling data

IF 3.6 2区 工程技术 Q2 TRANSPORTATION
Yudong Guo , Fei Yang , Siyuan Xie , Zhenxing Yao
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引用次数: 0

Abstract

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.

基于长短期记忆网络和手机信号数据的基于活动的模型
随着大数据时代的到来,基于活动的模型(ABM)再次成为交通规划领域的热门话题。交通大数据可以反映个人出行模式,从而为建立 ABM 提供了可能。然而,目前基于大数据的 ABM 还不成熟,尤其是在个人出行预测方面。因此,本文利用长短期记忆(LSTM)网络和手机信号数据提出了一种先进的 ABM。该模型为骨架调度模型,包含一级活动链和二级活动嵌套。然后提出了一个时间动态调整模型,用于调整连续活动之间的时间冲突。在成都进行了实地测试。工作和休闲出发时间的 KS 值分别达到 35.20 × 10-2 和 41.02 × 10-2,活动持续时间的 KS 值分别达到 44.91 × 10-2 和 54.65 × 10-2。结果表明,与现有的 BN、DT、GRNN、RF 和 GRU 相比,我们的模型能有效预测活动,并具有更好的准确性和稳定性。
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来源期刊
Transportmetrica A-Transport Science
Transportmetrica A-Transport Science TRANSPORTATION SCIENCE & TECHNOLOGY-
CiteScore
8.10
自引率
12.10%
发文量
55
期刊介绍: Transportmetrica A provides a forum for original discourse in transport science. The international journal''s focus is on the scientific approach to transport research methodology and empirical analysis of moving people and goods. Papers related to all aspects of transportation are welcome. A rigorous peer review that involves editor screening and anonymous refereeing for submitted articles facilitates quality output.
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