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We propose an analytical model to theoretically and numerically examine the impact of the introduction of e-hailing services into a region with traditional public transit, considering transit-dependent people who cannot drive themselves. For simplicity, this study considers many-to-one trip demand in a monocentric linear city. Our theoretical investigations confirm that in the short term when the service level of public transit is fixed, automobile users shift to public transit due to road congestion in the city center brought on by e-hailing services. For non-auto users, there exists an optimal connecting point where users transfer from feeder e-hailing services to public transit. Our theoretical analysis reveals that in the long term when public transit provider changes the service frequency according to the ridership, (1) the optimal connecting point remains at the same location, and (2) all public transit users in the suburb side of the optimal connecting point shifts to feeder e-hailing services. Our numerical investigations further reveal that the impact of e-hailing services on regional public transit depends on the regional dependency on automobiles. Shortly after introducing e-hailing services, the costs of users would significantly increase in a transit-dependent region, while the revenue of public transit significantly decreases in an automobile-dependent region. In the long term, the total cost for users is higher in all scenario settings compared to the base scenario without e-hailing services, which is analogous to the well-known Downs–Thomson paradox. Based on the above results, we discuss possible integrated regional transportation designs to realize desirable long-term outcomes. • Examine the impact of e-hailing services on existing transport systems. • Consider transit-dependent people as well as short- and long-term impacts. • Auto-dependency is a key determinant of the relationship between e-hailing services and public transit. • Illustrative examples intuitively reconfirm the theoretical implications. [ABSTRACT FROM AUTHOR]

Since the cost and time required to finetune parameters in traditional short-term traffic prediction models such as traffic simulators are very high, the prediction models have been developed mainly for managing recurrent congestion, rather than non-recurrent congestion caused, for example, by disaster. Machine learning models are promising candidates for traffic prediction during non-recurrent congestion due to their ability to tune parameters without a-priori knowledge, while their applicability to non-recurrent conditions has rarely been explored. To fill in this gap, this study conducts an exploratory analysis on the applicability of various machine learning models during a transportation network disruption with particular focuses on their ability to predict traffic states and the interpretability of the results. The analysis is conducted by using data obtained during the massive transport network disruption which occurred in Hiroshima in July 2018 due to heavy rain and subsequent landslides. The models tested include random forest, support vector machine, XGBoost, shallow feed-forward neural network, and deep feed-forward neural network. The results indicate that random forest and XGBoost methods produced the best results in terms of prediction accuracy. On the other hand, deep neural network models produce better results in terms of the interpretability of the results, i.e., the results can be logically explained from the perspective of existing traffic flow theory. These findings indicate that the model which produces the best prediction accuracy is not always the best for practical use since it does not mimic the mechanisms of congestion occurrence. • Apply machine learning models to non-recurrent congestion caused by disaster. • Explore their ability to (1) predict traffic states and (2) interpret the results. • XGBoost method produced the best results in terms of prediction accuracy. • Deep neural network models are better in terms of the interpretability. • Confirm the best prediction accuracy is not always the best for practical use. [ABSTRACT FROM AUTHOR]

Given the impending introduction of self-driving cars to Japan within the next several years, gaining a better understanding of public opinion and risk perception of autonomous vehicles (AVs) is crucial. Though AVs have numerous potential social and economic benefits, including reduced travel time and environmental impacts, their implementation will depend on public acceptance. This study expanded on existing work by directly examining which aspects of AV use and function most affect risk perception. Participants were shown short animated video clips depicting the introduction of AVs into society at large, as well as three specific possible risk factors: system error, external interference with car controls (i.e., hacking), and the inability of the car to cope with unexpected events. Participants were then surveyed about their attitudes toward AVs and other potentially risky activities and technologies. The study established that the perceived advantages of all AV types (cars and buses, different automation levels) outweighed their perceived risks. Consistent with prior research, the two major aspects of perceived risk were dread and unfamiliarity. The results showed compared with other technologies, AV scores were neutral for dread risk but higher for unfamiliarity risk. The finding of high unfamiliarity indicates that public acceptance and perceived risks are likely to change as the public's knowledge increases. We also found that receiving information about a potential system error indirectly reduced AV acceptability, where dread and unfamiliarity to the AV risks served as mediators. The results suggest that proper management on the diffusion of information, which includes public information campaigns, test-ride events and transparency about safety options, will likely influence the ultimate social acceptability of AVs and will be crucial towards its successful introduction on the market. [ABSTRACT FROM AUTHOR]

This study analyzes the association between individual capabilities and travel time in Mumbai. We formulate a conceptual hypothesis by differentiating the production (e.g., commuting) aspects from the consumption (e.g., leisure) aspects of travel. We argue that less capable people may focus more on the production aspects, whereas more capable people may focus more on the consumption aspects. Two operationalized hypotheses are introduced for empirical verification: (1) travel time is significantly related to individual capability and is shorter for less capable people, and (2) the variance of travel time, indicating the degree of freedom of movement, has a positive association with individual capability. To confirm the hypotheses empirically, an activity–travel survey was conducted in middle-income group housing, slums, and slum rehabilitation units in Mumbai, India. Our results support both hypotheses, suggesting that, when people are less capable, they attach more importance to the production aspects of travel, but as capability increases, the consumption aspects become more vital. Based on our findings, we discuss practical implications for transport appraisal in the development context. [ABSTRACT FROM AUTHOR]

Innovative technologies such as autonomous and connected vehicles can create a better transit mall, a street where only public transit vehicles, bicycles, and pedestrians are allowed. However, successful introduction of such technologies depends on user acceptance. To assess the potential acceptance of these technologies in the context of a new transit mall environment, it is crucial to provide an opportunity to experience them before they roll out. This study explores how real and video-based experiences of new technologies can affect public acceptance of new transit mall options in Hiroshima City, Japan, where trams and buses are significant modes of public transportation. We conducted a survey on the stated acceptance of new transit mall options with three groups of respondents: real experience, video-based experience, and no experience groups. The real-experience group self-selected themselves for the experiment, which potentially caused bias. Thus, we used a hybrid choice model with attitudinal variables (ethical, legal, and social concerns regarding emerging technologies, as well as risk-related factors) introduced to control for this bias. We found that both real and video-based experiences improved overall acceptance of new transit mall options, but there were differences in preferences between those who had only video-based experiences and those who had real experiences. We also found that prior knowledge of new transport services and infrastructure had a considerable influence on stated acceptance. [ABSTRACT FROM AUTHOR]

Comfort could be one of the major factors influencing demand for public transit systems across income groups. Those in the high-income group, typically car owners, value comfort more than those in the traditionally captive low-income group. This paper examines the heterogenous effect of crowding on mode choice and the subsequent demand for mass rapid transit (MRT) system across income groups using stated-preference data collected from Dhaka, Bangladesh. The crowding variable was incorporated into mixed logit models as a Bureau of Public Roads (BPR)-type function, establishing non-linear sensitivity of different income groups to the effect of crowding on mode choice. We then estimate equilibrium passenger flows for different income groups for multiple scenarios based on varying levels of fares and capacity under the stochastic user equilibrium (SUE) condition. The consumer surplus, revenue, and total surplus associated with each scenario were calculated. The findings show that fares that maximize social welfare are not equitable as they result in very low MRT demand among the low-income group. Meanwhile, higher fares result in greater demand for MRT among the high-income group, improved social welfare, and less total car use. These results highlight the trade-off between an equitable public transport system and environmental sustainability. [ABSTRACT FROM AUTHOR]

While most existing closed-form discrete choice models can be regarded as special cases of McFadden's generalized extreme value model, recently, alternative frameworks of McFadden's generalized extreme value model, which maintain closed-form expressions, have been proposed; these include the weibit model, which uses the Weibull distribution for its random component. In this paper, we develop a generalized closed-form discrete choice model which include both logit and weibit models as special cases, by introducing the q-product random utility, in which the relationship between the systematic component and the random component can be either additive, multiplicative, or in-between, depending on the value of the parameter q . We show that, when imposing the Gumbel distribution on its error component (instead of assuming the additive case as the logit model), the parameter q depicts decision maker's risk attitude in the sense of the Arrow–Pratt measure of relative risk aversion, which would be a behavioral foundation of the model. We also show that the model can be straightforwardly extended to incorporate statistical dependence across alternatives. The performance of the proposed model is examined by using two case studies; one on travel-route choices and the other on transport-mode choices. [ABSTRACT FROM AUTHOR]

This study proposes a generalized multinomial logit model that allows heteroscedastic variance and flexible utility function shape. The novelty of our approach is that the model is theoretically derived by applying a generalized extreme-value distribution to the random component of utility, while retaining its closed-form expression. In addition, the weibit model, in which the random utility is assumed to follow the Weibull distribution, is a special case of the proposed model. This is achieved by utilizing the q -generalization method developed in Tsallis statistics. Then, our generalized logit model is incorporated into a transportation network equilibrium model. The network equilibrium model with a generalized logit route choice is formulated as an optimization problem for uncongested networks. The objective function includes Tsallis entropy, a type of generalized entropy. The generalization of the Gumbel and Weibull distributions, logit and weibit models, and network equilibrium model are formulated within a unified framework with q -generalization or Tsallis statistics. [ABSTRACT FROM AUTHOR]

Abstract: Based on Japanese panel data derived from 12 monthly waves and 1253 questionnaires, the study examines the impact of social interactions on tourism participation. Social interactions are classified into three types: namely, endogenous, exogenous and correlated effects. It is empirically confirmed that endogenous social effects have significant influences on tourism participation behavior. For example, it is found that interactions with people of the same income groups (an example of endogenous social effects) are generally significant across most months of the year. However, it is worth noting that endogenous social effects will be overestimated if the correlated social effect is not taken into account. The results suggest that social multiplier effects can be discerned that have potential implications for promotion and management of tourism participation rates. [Copyright &y& Elsevier]

• Logit model is doubly generalized (q -generalized) • Utility follows the generalized extreme value distribution including Gumbel & others. • Utilities are multivariate, and dependency of alternatives are allowed. • Correlation structure of multivariate utilities is analytically obtained. • Our model system has a closed-form and parameterized/statistically-unified expression. In this study, we generalize our previous q -generalized multinomial logit model (Nakayama & Chikaraishi, 2015), in which the heteroscedastic variance and flexible shape of the utility distribution are considered, by allowing for statistical dependency of alternatives. This is achieved by introducing the q -generalization of McFadden's multivariate Gumbel distribution. Thus, the logit model is doubly generalized; 1) each utility follows the generalized extreme value distribution that includes the Gumbel, Weibull, and Fréchet distributions; and 2) the utility distribution is multivariate, and therefore, a nested or cross-nested structure and dependency of alternatives are allowed. The proposed doubly generalized logit model system allows for deriving new closed-form discrete choice models such as the q -generalized nested logit model and q -generalized cross-nested logit (CNL) model. Furthermore, the model system includes conventional logit models such as the multinomial logit, nested logit, and CNL models as special cases as well as the new generalized logit models, while retaining a closed-form expression. We empirically confirm that the goodness-of-fit of the proposed model could be substantially better compared to that of the conventional models in some cases, though the degree of improvements varies across cases. [ABSTRACT FROM AUTHOR]

Understanding and predicting post-disaster human movements is critical for evaluating a population's vulnerability and resilience and developing plans for disaster evacuation, response and recovery. In this study, we attempt to analyze population movement by using mobile spatial statistics. In order to extract behavior patterns from the aggregated data, we use four different Latent Variable Analysis (LVA) methods - Independent Component Analysis (including FastICA and Spatial colored ICA), Non-negative Matrix Factorization (NMF), and Sparse Principal Component Analysis (SPCA) to analyze mobile statistics data of the disaster-affected area. The results indicate that each LVA methods has its pros and cons in extracting behavior patterns from the aggregated population. We conclude that, using multiple LVA methods and finding out the common patterns would be a robust way to understand and explain population dynamics. Finally, we argue that using mobile spatial statistics would be a feasible and practical option to estimate the dynamic change of human population after the occurrence of disasters. [ABSTRACT FROM AUTHOR]

An idea key to human-centric city planning is the reclamation of urban spaces from vehicles and their redesign for human activities. Aligned with this concept, this study presents a framework for human-centric network design. The framework is developed based on a multi-objective optimization model that designs pedestrian zones within a city center network considering conflicting policy objectives. Design performance is evaluated through the interaction between the design and the behavior of network travelers. A vehicle–pedestrian multimodal network equilibrium assignment model is constructed to this end. To efficiently search for better designs, we also develop a hyper-heuristic based on the adaptive large neighborhood search algorithm that dynamically adjusts the probability of selecting operators for neighborhood search. The framework was applied to a city center network in Kawagoe City, Japan, where the conflict between vehicular and tourist pedestrian traffic has long been considered a major problem. Our algorithm successfully found a set of Pareto frontier solutions that clearly show the trade-off between conflicting objectives. A balanced network design among the frontier solutions improved pedestrian comfort by 64.7% while increasing vehicular travel time by only 3.8%. It would be ideal for municipalities to improve the pedestrian experience while maintaining current levels of convenience to drivers, but both issues must be weighed against severe road space constraints. Our approach can aid the discussion on this trade-off by providing a set of Pareto frontier solutions, as each frontier solution shows a different trade-off pattern and can be considered as a meaningful design alternative for policymakers. [ABSTRACT FROM AUTHOR]

• The price elasticity of parking demand in the Japanese coin-parking market was estimated. A field survey of parking prices and demand was conducted. • A Heckman-type sample selection model with instrumental variables were used. • Price elasticity estimates ranged from about –1.683 to –0.9971. Existing evidence suggests that the demand for parking is inelastic. This study investigates the price elasticity of parking demand in the Japanese coin-parking market, which is characterized by near-free-market conditions. A field survey of parking prices and demand in an approximately 8 km2 area in the center of Hiroshima City was conducted. The prices per 10 min were collected from 949 parking lots, and occupancy rates were observed for 133 parking lots from afternoon to evening. The estimation results, using a Heckman-type sample selection model with instrumental variables, reveal that price elasticity ranged from –1.683 to –0.9971, which is higher than the estimates of previous studies in terms of absolute value. This difference can be attributed to the characteristics of the target area where alternative parking spaces and transportation options are readily available. Our results also indicate that parking operation companies have difficulty raising price in areas with high parking density due to competition, resulting in the increase in the attractiveness of automobiles relative to public transportation. Parking price would need to be controlled to manage urban traffic. [ABSTRACT FROM AUTHOR]

The influence of land use policy on people's residential and travel choices may be overestimated because of self-selection effects. In the context of commuting, neighborhood and travel preferences may induce self-selection effects in choices about residential location, work location, and commuting mode. Presumably, such self-selection effects may vary across different job markets. To date, however, no study has been done in developing countries. Therefore, this study aims to fill this research gap by using data collected in Hanoi, Vietnam. A joint model with the above three choices is built by incorporating self-selection effects, where effects of land use attributes are emphasized. Choices of labor-intensive workers (11,344) and knowledge-intensive workers (12,360) are compared. The statistical significance of multiple self-selection effects is confirmed, which suggests that the joint estimation of the above three choices is a useful approach. As for the magnitude of influence of self-selection effects, self-selection seems to be more influential in knowledge-intensive workers' residential location and work location choices. As for land use attributes, different types of households, and labor-intensive and knowledge-intensive workers show different responses to different types of land in location choices (especially the work location choice). Effects of land use diversity and population density on the commuting mode choice are mixed. Additionally, the centralization of knowledge-intensive employment and decentralization of labor-intensive employment are captured. These findings may be useful for city planners in Hanoi in designing land use patterns in the future in order to keep knowledge-intensive workers working and living close together. [ABSTRACT FROM AUTHOR]

• Explored slow behaviors in smoke via video observations and physiological signals. • Investigated the influence of negative emotions on slow speed via the DID model. • Individuals with slow speeds did not consist only of disaster weak people. • Negative emotions had a more pronounced impact on individuals with slow speeds. • Observed that 15.3% of the slow participants paused several times during evacuation. This study delved into the fundamental relationship between negative emotions and slow walking behaviors in a smoke-filled model-scale tunnel. Behavioral patterns and emotional responses were investigated by video observations and physiological signals. Contrary to previous assumptions, it was revealed that the "slow" was not solely indicative of older people, children, or individuals with disabilities. 15.3% of the slow participants paused several times during evacuation, with those in stress and low-stress cases pausing to look around, whereas a portion of those in fear and anxiety cases did nothing during their pause, potentially linked to threat-induced states such as orienting, freezing, and tonic immobility. 20.7% evacuated while stooping, possibly because of participants' background (the disaster prevention education in Japan). The Difference-in-Differences (DID) estimation results indicated that negative emotions exerted a more pronounced influence on slow speeds compared to the overall speeds. [ABSTRACT FROM AUTHOR]

• Conduction of walking speed experiments by a full-scale tunnel with smoke. • Walking speeds were almost slow and very slow. • Validation of physiological signal-based negative emotion by subjective feeling. • Negative emotion degree investigated by physiological signal-based negative emotion. • Low score of stress tolerance impacted to the negative emotion increase. In this study, the subjective feelings from questionnaires, the physiological signals-based emotions, and walking speed were investigated by a full-scale smoke-filled tunnel experiment. The physiological signals-based emotions were compared by the subjective feelings. The present mean speed was slower than the results of previous studies regarding evacuation in smoke, and close to the completely darkened walking speed, even the participants who were familiar with tunnels. The negative emotion degree via physiological signals, which was determined by the heart rate change rate, was anxiety > fear up to Cs = 1.0 m−1 and then switched to fear > anxiety over 1.0 m−1. It could be considered that the fear might be large with smoke density due to their present threat (not future threat). The tendency of the mean walking speed by negative emotion via physiological signals was the same as the negative emotion degree in smoke. [ABSTRACT FROM AUTHOR]

• Experimentally investigated emotions and walking speed in a smoke model tunnel. • Measured negative emotions using physiological signals. • Explored the relationship between emotion and walking behavior through SEM. • Negative emotions impacted slow walking behavior. • Negative emotions increase heart rate, similar to the Proulx's model order. This study investigated the relationship between emotion and evacuation behavior in smoke for future modeling of irrational behavior by conducting experiments using a model-scale smoke-filled tunnel. Furthermore, the causal relationship between emotions and walking behavior was investigated through structural equation modeling. The results show that emotions influence slow walking behavior. The increase in heart rate due to the walking exercise was smaller than that caused by emotions. Moreover, the heart rate change degree with emotion corresponded to Proulx's proposal order, from low-stress to anxiety. [ABSTRACT FROM AUTHOR]

Individuals undertake both solo and joint activities as part of their overall activity-travel patterns. Compared to work and maintenance activities, social and leisure activities differ in that they exhibit high levels of temporal and spatial flexibility. In this study we used data from an ego-centric social networks survey in the Greater Tokyo Area and a follow-up group activity survey to estimate a joint eating-out destination choice model explicitly incorporating group-level impedance. We tested four different measures of group-level travel time (maximum travel time, minimum travel time, group average travel time and group median travel time) and evaluated their performance against a model using only ego travel time. Estimation results show that models incorporating centrality measures of group impedance generally outperform ego-only models, with the best performing travel time measure being group average travel time. The best performing model increased model performance against the ego-model up to 32.2% in terms of fitting factor, a substantial increase that underscores the need to incorporate group-level characteristics in travel behavior models. [ABSTRACT FROM AUTHOR]