Autonomous mobility-on-demand (AMoD) has the potential to improve first- and last-mile connectivity in multimodal metropolitan settings. Prior studies have examined many-to-one and many-to-few AMoD services from perspectives such as operational efficiency, user preferences, and system-wide demand impacts. However, research that integrates relevant preference data to evaluate system-wide demand impacts, particularly concerning interactions with railway systems, remains limited. This study simulates the effects of introducing autonomous vehicle-based on-demand services to and from rail stations (hub-based AMoD) on travel demand in a multimodal metropolitan setting using the Tokyo metropolitan area as a case study. Stated preference data on rail users’ intentions to use hub-based AMoD services were incorporated into an existing travel demand forecasting model, and the resulting changes in station accessibility and rail demand were examined. The results show that the density of rail trips using hub-based AMoD is higher in central areas, as well as in surrounding areas where the share of hub-based AMoD in the first- and last-mile market is relatively large. These surrounding areas often have conditions that discourage walking or cycling, such as notable elevation differences. The percentage increase in rail trip generation was greater in suburban areas than in central areas, and greater for intra-regional trips than for longer-distance inter-regional travel. By distance from stations, the increase in rail trip generation is largest within 1.8–2.5 km and smallest within 700 m. Overall, the findings indicate that the demand impacts of hub-based AMoD depend on local first- and last-mile conditions, providing insights into suitable deployment strategies for improving station access and supporting rail use.

This study explores safe and efficient coexistence of autonomous vehicles (AVs) and pedestrians in mixed-use traffic environments. Using an evolutionary game theory framework with imitation and best-response dynamics, respectively, and under the assumption of heterogeneous risk perceptions among pedestrians, spontaneous collective behavioral patterns which can be formed as their interactions are analyzed. Numerical results indicate that fully autonomous decision-making vehicles may better avoid social dilemmas such as the "Freezing Robot Problem" and promote cooperation. The study emphasizes the importance of aligning AV behavior with suitable environmental and traffic rule conditions for the successful integration of AVs into public spaces.

This paper evaluates the benefit of integrating vehicle-based mobile crowd-sensing tasks into the ride-hailing system through the collaboration between the data user and the ride-hailing platform. In such a system, the ride-hailing platform commissions high-valued sensing tasks to idle drivers who can undertake either ride-hailing or sensing requests. Considering the different service requirements and time windows between sensing and ride-hailing requests, we design a staggered operation strategy for ride-hailing order matching and the sensing task assignment. The auction-based mechanisms are employed to minimize costs while incentivizing driver participation in mobile sensing. To address the budget deficit problem of the primal VCG-based task assignment mechanism, we refine the driver selection approach and tailor the payment rule by imposing additional budget constraints. We demonstrate the benefits of our proposed mechanism through a series of numerical experiments using the NYC Taxi data. Experimental results reveal the potential of the mechanism for achieving high completion rates of sensing tasks at low social costs without degrading ride-hailing services. Furthermore, drivers who participate in both mobile sensing tasks and ride-hailing requests may gain higher income, but this advantage may diminish with an increasing number of such drivers and higher demand for ride-hailing services.

Activity-based models (ABMs) have been applied to analyzing large-scale and detailed human mobility patterns. However, previous studies have highlighted that the ABM simulator faces challenges in accurately reproducing real-world conditions. In this study, we integrated a deep learning model, a variational autoencoder, with an ABM simulator to perform large-scale parameter calibration in a single step, without modifying the aggregate population data. This integration enabled the development of a calibration framework that updates specific model parameters. To allow learning using the backpropagation method in deep learning, we employed a differentiable sampling method from a multinomial distribution using the Straight-Through Gumbel Softmax trick. We implemented a selection behavior process based on the utility functions of each option within the simulator. We conducted a case study using a submodel of an existing ABM simulator for the Tokyo Metropolitan Area and real-world aggregated population statistics, and showed that it can be applied to calibrate large-scale ABM simulators.

E-commerce marketplaces often offer fast and scheduled delivery services for free to attract customers, imposing strict time constraints on delivery, but do all people really want such services? This study aims to quantitatively analyze heterogeneous customers' preferences for home delivery timing. We propose two willingness-to-pay measures, namely, the value of delivery time savings (VODT) and the value of time slot shortening (VOTS). We capture their distributions in a data-oriented manner by estimating an extended mixed logit model with a semi-nonparametric approach. The estimated VODT ranged from -47.9 to 219.4 JPY/day, with a median of 25.6 JPY/day. This result shows a large taste variation and that most customers were not willing to pay even 10% of the delivery fee (set between 300–600 JPY) to save the waiting time by one day. Moreover, VOTS was found to be low, distributed with a median of 5.0 JPY/hour. The fact that some customers do not necessarily highly value the reduction in delivery lead-time or time slot size sheds light on demand management to reduce delivery burden while maintaining customer satisfaction. In addition, the valuation varied according to the category and price of the purchased good as well as the e-shopping frequency of customers, which suggests a possibility of differentiated pricing or real-time design of service attributes as a retailing strategy.

Ride-hailing has been introduced and has become popular in many major cities worldwide. The service often relies on the use of dynamic pricing, in which fares are adjusted in real time. Therefore, understanding the impact of fare on demand is necessary for the operation of ride-hailing. The aim of this study is to empirically investigate the impact of fares on demand through price elasticities using the session data of Uber taxis from Uber Japan's experiments in two cities: Nagoya and Kyoto. A mixed logit model with a flexible mixing distribution was estimated to capture the taste heterogeneity among riders, which increased the reliability of the result. The estimation results indicated that most riders were price inelastic, with an average price elasticity of approximately −0.2 to −0.1. These findings are useful for ride-hailing companies and policymakers because they provide valuable information to enable the maximization of profit or benefit to customers.

Perimeter control involves monitoring network-wide traffic and regulating traffic inflow to alleviate hypercongestion. Implementation of transit priority with perimeter control measures, which allow transit into a controlled area without queuing at the perimeter boundary, is an effective strategy in bimodal transportation systems. However, travelers' behavior changes in response to perimeter control strategies, such as shifts in their departure times and transporta-tion modes, have not been fully investigated. Therefore, important questions remain, such as the use of transit during perimeter control with transit priority. This paper examines the travelers' behavior changes in response to perimeter control with transit priority in a mixed bimodal transportation system with cars and flexible route transit (FRT) vehicles. We model departure time and transportation mode choices in such a transportation system with hypercongestion and discomfort in FRT (called the mixed bimodal bathtub model). Initially, we investigate the properties of dynamic user equilibrium without perimeter control. Then, we study the equilibrium patterns during perimeter control with transit priority. Unlike existing works, we find that the number of FRT passengers decreases with time toward the desired arrival time and that FRT may not be used around the peak of rush hour. Furthermore, transit priority may not be sufficient to promote the use of FRT, and additional incentive such as subsidy for lower fares may be required to encourage FRT use during perimeter control. Finally, we show that operating many FRT vehicles does not always decrease the equilibrium cost, even under perimeter control with transit priority.

本研究では,ポストコロナにおける都市圏の通勤鉄道需要の構造変化とラッシュアワーにおける時間帯別課金導入の影響分析を念頭に置いたマルチクラス乗客配分モデルを構築した.具体的には,2 種類の通勤スタイルが異なる通勤者を想定し,その出発時刻選択に関する均衡配分モデルと最適課金導出のためのシステム最適配分モデルを構築した.ポストコロナを想定した 3 種類の需要構造変化シナリオ((1) 在宅勤務の増加,(2) フレックス勤務体制通勤者の増加,(3) 混雑に対する抵抗感の増加)のもとでシミュレーションを行い,コロナ前の状況に比べて混雑率や課金がどのように変化し得るのかを明らかにした.最後に,構築したモデルを実路線に適用し,今後起こり得る通勤者の出発時刻選択行動変化の可能性を示した.

本論文では,近年方法論の展開が大きく進んでいる異質性と摂動性という観点に特化して離散選択モデル研究の包括的なレビューを行い,今後の行動モデル研究についての展望を示すことを目的とする.まず,異質性に関しては,Mixed Multinomial Logit モデルを理論の下敷きとして,個人の選好の異質性を離散選択モデルの枠組で具体的に記述し,詳細な個人データに基づいて推計するための近年の方法論開発についてレビューを行う.次に,摂動性に関しては,摂動効用の概念を概説した上で様々な意思決定の場面への応用可能性を示すと共に,一般化エントロピー,凸共役性,需要関数の可逆性等の概念を鍵とした需要推計方法論の展開について包括的に整理する.最後に,レビューを総括した上で,今後の行動モデル研究の展望について論じる.

Many e-commerce marketplaces offer their users fast delivery options for free to meet the increasing needs of users, imposing an excessive burden on city logistics. Therefore, understanding e-commerce users' preference for delivery options is a key to designing logistics policies. To this end, this study designs a stated choice survey in which respondents are faced with choice tasks among different delivery options and time slots, which was completed by 4,062 users from the three major metropolitan areas in Japan. To analyze the data, mixed logit models capturing taste heterogeneity as well as flexible substitution patterns have been estimated. The model estimation results indicate that delivery attributes including fee, time, and time slot size are significant determinants of the delivery option choices. Associations between users' preferences and socio-demographic characteristics, such as age, gender, teleworking frequency and the presence of a delivery box, were also suggested. Moreover, we analyzed two willingness-to-pay measures for delivery, namely, the value of delivery time savings (VODT) and the value of time slot shortening (VOTS), and applied a non-semiparametric approach to estimate their distributions in a data-oriented manner. Although VODT has a large heterogeneity among respondents, the estimated median VODT is 25.6 JPY/day, implying that more than half of the respondents would wait an additional day if the delivery fee were increased by only 26 JPY, that is, they do not necessarily need a fast delivery option but often request it when cheap or almost free. Moreover, VOTS was found to be low, distributed with the median of 5.0 JPY/hour; that is, users do not highly value the reduction in time slot size in monetary terms. These findings on e-commerce users' preferences can help in designing levels of service for last-mile delivery to significantly improve its efficiency.

MaaS (Mobility as a Service) には,異なる交通モード間で情報や料金体系を統合して利便性を上げることで,人々の外出機会の増大を促進する役割も期待されている.本研究では,MaaS 導入による人々の活動変化を評価するために,田淵・福田 (2020) を拡張し,定額料金の支払いで一定エリアの交通サービスが利用し放題になるサブスクリプションサービスを明示的に考慮した鉄道利用者の Activity-based モデルを構築した.東京都市圏を対象に,MaaS 導入による人々の活動変化予測とそのときのサブスクリプション料金水準を推計するシミュレーションを行ったところ,MaaS が寄り道を促進させる効果が一定量あることや,サブスクリプション料金への支払い意志額は女性や20歳以下の若者ほど高くなることなどが示唆された.

Traditional discussions of public transport management in Japan have been primarily based on profit concerns. However, in recent years, the discourse has shifted to incorporate social capital considerations, with greater attention given to the relationship between public transport and social capital. The idea is that by increasing mobility, public transport can facilitate social activities and foster networks and trust among people. This makes it a valuable tool for building social capital, particularly in depopulated areas that are at risk of losing their local networks. This study aims to investigate whether there is a positive correlation between the use of public transport and social capital at the regional level in Japan, using a quantitative method. We examine municipal-level data from all municipalities in Japan and we find a strong and positive correlation between the use of public transport and social capital. These results have significant implications for policymakers seeking to manage Japan's public transport system, especially in rural areas. Our findings suggest that policymakers should shift the focus from purely economic benefits to also prioritize social benefits.

This chapter reviews recent theoretical and empirical studies on road congestion pricing and provision of capacity, in particular, second-best pricing, modeling dynamic congestion, and capacity choice. We discuss the basic principle of the second-best pricing, and summarize the contributions of cordon pricing, value pricing, and more sophisticated pricing schemes relying on information technology. Regarding dynamic congestion, there are various extensions of the bottleneck model, and a new strand of research on the macroscopic fundamental diagram (MFD) or bathtub model. We describe the basic theory of capacity choice with optimal pricing, then review subsequent studies on the second-best capacity choice, decentralized provision of capacity. Empirical research based on innovative data collection methods combined with advanced econometric techniques provides a more accurate evaluation of road pricing policies.

近年,デジタル技術の普及や多様な働き方の推進により,社会変容が加速している.こうした状況を踏まえ,従来のように四段階推計法を用いて人の行動を集計量として捉えるだけではなく,個人の行動特性を把握しながら,社会の実態に即した検討が必要となっている.本研究では,個人の一日の活動を再現しトリップの連関性を考慮できるアクティビティシミュレーションと,近年その種類と量が飛躍的に増加している交通状態の観測データを用いて,低コストでより精度の高い政策評価や需要予測を可能とする推計手法の提案を行った.東京都市圏全体に対して本手法を適用することにより,予測精度が向上し,アクティビティシミュレーションに対して観測データを補完する有効性が確認された.

Unlike the lockdown measures taken in some countries or cities during the COVID-19 outbreak, the Japanese government declared a "State of Emergency"(SOE) under which people were only requested to reduce their contact with other people by at least 70%, while some local governments also implemented their own mobility-reduction measures that had no legal basis. The effects of these measures are still unclear. Thus, in this study, we investigate changes in travel patterns in response to the COVID-19 outbreak and related policy measures in Japan using longitudinal aggregated mobile phone data. Specifically, we consider daily travel patterns as networks and analyze their structural changes by applying a framework for analyzing temporal networks used in network science. The cluster analysis with the network similarity measures across different dates showed that there are six main types of mobility patterns in the three major metropolitan areas of Japan: (I) weekends and holidays prior to the COVID-19 outbreak, (II) weekdays prior to the COVID-19 outbreak, (III) weekends and holidays before and after the SOE, (IV) weekdays before and after the SOE, (V) weekends and holidays during the SOE, and (VI) weekdays during the SOE. It was also found that travel patterns might have started to change from March 2020, when most schools were closed, and that the mobility patterns after the SOE returned to those prior to the SOE. Interestingly, we found that after the lifting of the SOE, travel patterns remained similar to those during the SOE for a few days, suggesting the possibility that self-restraint continued after the lifting of the SOE. Moreover, in the case of the Nagoya metropolitan area, we found that people voluntarily changed their travel patterns when the number of cases increased.

Urban rail transit often operates with high service frequencies to serve heavy passenger demand during rush hours. Such operations can be delayed by two types of congestion: train congestion and passenger congestion, both of which interact with each other. This delay is problematic for many transit systems, since it can be amplified due to the interaction. However, there are no tractable models describing them; and it makes difficult to analyze management strategies of congested transit systems in general and tractable ways. To fill this gap, this article proposes simple yet physical and dynamic model of urban rail transit. First, a fundamental diagram of transit system (i.e., theoretical relation among train-flow, train-density, and passenger-flow) is analytically derived considering the aforementioned physical interaction. Then, a macroscopic model of transit system for dynamic transit assignment is developed based on the fundamental diagram. Finally, accuracy of the macroscopic model is investigated by comparing to microscopic simulation. The proposed models would be useful for mathematical analysis on management strategies of urban rail transit systems, such as optimal dynamic pricing for travel demand management.

This study empirically analyzes the impact of working from home (WFH) on travel behavior in the Tokyo metropolitan area. We use a large survey sample divided by the usual travel mode for commuting and quantify the impact of WFH on the number of rail, car, and walking and cycling trips made on a weekday. Two types of trip frequency models are examined: (1) a multivariate Poisson-lognormal (MVPLN) regression model that simultaneously explains the number of trips made by multiple modes and (2) a negative binomial regression (NBR) model. Explanatory variables comprise the place of work, the built environment of the place of residence, and individual and household attributes. The estimation results of the MVPLN model show that the error correlation between the number of trips made by a commuting mode and that by other/non-commuting modes is low for both rail and car commuters, which could justify the application of an NBR model. The estimation results of the NBR model show that the average effect of WFH for the full day (compared with working only outside the home) is a reduction of 1.9 rail trips per day for rail commuters and 1.6 car trips per day for car commuters, with car commuters who live in low-density areas tending to reduce car trips to a lesser extent. Meanwhile, few differences are observed in the reduction in rail trips for rail commuters by population density. Rail commuters tend to walk and cycle more if they work from home for the full day.

ドライバーに不完全な交通情報を提供したときにハンチングという交通量の振動現象が生じることが知られている．Iwase et al.1) はこれを回避する情報提供方式である自己実現シグナルの概念を提案した．しかし，単純な解析モデルを用いた考察に留まり，人間が実際にシグナルを受けて経路選択した場合の効果は未知である．本研究では，小規模室内実験を実施して自己実現シグナルによるハンチング抑制の現実的妥当性を検証した．簡単な仮想ネットワークにおける室内実験環境を構築し，被験者が集団で経路選択ゲームを行う実験から得たデータを解析し，部分的ながらもシグナルによるハンチング抑制効果があることを確認した．次に，実験データの代表的な経路選択規範を模擬した数値シミュレーション分析を行い，選択行動とシグナル効果の関係性を明らかにした．