As an introduction of the international research workshop, Prof. Hato started with the example of the gridlock phenomenon which occurred just after The Great East Japan Earthquake, in Tokyo metropolitan area. In the collaborative research with Mr. Oyama, they conducted an analysis on the drivers' behavior in damaged networks. In such a context, drivers have little spatial information and tend to choose routes shortsightedly. In order to model these decision-making dynamics, they proposed a discounted recursive logit model. Also, he introduced on-demand routing optimization method using network contraction, which will contribute to the new transportation system in the depopulated areas. He concluded that route choice model with sequential discount rate is beneficial for understanding the decision dynamics in unsteady networks.

He analyzes the tsunami evacuation destination choice process, using the Spatial Correlated Logit model. He conducted a case study of the Great East Japan Earthquake based on the data collected from Kesennuma city, in Miyagi prefecture. In order to describe an understudied aspect of the evacuation process, he improved SCL model so that it can account more accurately for spatial correlation among alternatives. The results suggest the existence of a high degree of correlation among unobserved attributes of zones, and empirical findings suggest that factors such as OD distance by mode, OD altitude difference, number of buildings and number of officially designated shelters are statistically associated with evacuation destination choice. Also, and from a theoretical perspective, it addresses the issue of spatial correlation in discrete choice models. A spatially correlated logit model is estimated, where the allocation parameter is specified as a function of proximity and inter-zone altitude difference to capture more adequately unobserved similarities among alternatives in the specific context of tsunami evacuation, and the proposed model yielded better statistical fit than the traditional MNL.

For the pre-disaster planning, it is necessary to understand evacuation activity. Under the Great East Japan Earthquake in 2011, some irrational evacuation behaviors were observed in the trip data collected from survivors in Rikuzentakata-city. But in the current evacuation models, they do not consider time constraints and future utility, and time is regarded as "timing", although in an emergency situation it should be considered as "resource to use". In order to model the evacuation behavior more accurately, she introduced Dynamic Discrete-Continuous Model and improved the former model by incorporating 2 new points: Time Discount Rate Beta and Time Constraints. She conducted a case study in Rikuzentakata-city, using survey data collected in 2012. The main findings through the estimation are threefold. First, elderly people are more likely to choose evacuation and their evacuation time is short. Second, people living closer to the coastline are likely to finish all activities immediately but people living far from the coastline tend to evacuate later, which signifies the demand for evacuation overlaps near the shelter. Third, in the evacuation behavior, time discount rate becomes smaller than in ordinary situation.

Focusing on the interaction generated at the time of disaster situation, he clarified how much the interaction influences the decision to evacuate. He improved network formation model in order to describe a network formation in a small village under the disaster risk, and he formulated interaction in a small community by introducing Cross Nested Logit model (CNL). After that, he evaluated the influence on evacuation timings using local interaction model, while considering the positive & negative interaction between a robust person and a weak (disabled or elderly) person. In the formulation of an evacuation model, the utility function contains social utility which is defined by a human network and weight of interaction, and here, the weight of interaction can catch the asymmetric (altruistic) interaction between a robust person and weak person. He also carried out a case study in the mudslide case in Niihama city, occurred in 2004. The estimation result suggests that evacuation alert for the weak from rescue and number of the direct interaction encourage their evacuation and the robust people hesitate to evacuate before the weak evacuate. Through numerical simulation, he had two suggestive conclusions. First, staying trends will be amplified by gathering people who have low evacuation probability. Second, evacuation of a weak person has a positive impact on others' evacuation because the robust are able to start their evacuation.

Tomer began his presentation with the introduction of the precedence research on evacuation behavior and explained about the Haifa fire event which occurred on November, in 2016. He conducted a web-based survey to collect information about the evacuation. Based on the information, he estimated binary evacuation choice model. The result shows that people who live with children under 12 are more likely to choose evacuation. In addition to that, the strong social network effect is observed because there was strong tendency to travel together to the shelter.

Ido talked about his preliminary research on activity patterns and intra-household interactions in evacuation behavior, he aims to implement activity-based models for disaster, while focusing on intra-household interactions during the evacuation. In Haifa fire (2006) case, he carried out some preliminary analysis and there was a big difference in behavior between adults with children and adults without children because adults with children tend to make more stops to pick up their children, therefore, it is necessary to consider intra-household interactions.

In the presentation, she developed a frequency based transit assignment model considering that online information of predicted arrival times is available to passengers. Some public transportations have introduced technologies which informs the passenger of predicted arrival times, and transit assignment plays an essential role in planning and management of transit networks. The methodology is considering two types of available online information: (1) full, where the arrival times are available for all intermediate stops in the candidate paths, and (2) partial, where the arrival times are available at the boarding stop only. Passengers are assumed to consider the estimated arrival times together with the expected travel time when choosing their path. The assignment procedure includes the finding of attractive paths, setting of route choice decision rules for different cases of predicted arrival times, and the probability calculation for these different cases. The developed model is illustrated by an application for the Winnipeg network. In comparison to the well-known optimal strategies method, the suggested model produced significantly different assignment results and a notable reduction in the total travel time. The results demonstrate the potential impact of online information on travel behavior and route choice and emphasize the need for its consideration in planning models.

Professor Tomer introduced micro-simulation for developing Autonomous Vehicles (AVs) to evaluate expected effects on traffic flow, safety, and externalities. In the simulation, driving models need good algorithms for autonomous driving that is longitudinal-lateral movement. Then, there are some challenges that are how to set variants, how to treat sensing and communications, and how to idealize human driving in semi-AVs. They conducted the experiment in which they checked Adaptive Cruise Control(ACC) state and vehicles movement. Applying these data to Mixed logit choice model, they analyzed the relation between ACC transitions and some parameters. After that, they run a simulation to check the harmonic speed changing ACC and C-ACC penetration rates, demand level, and automated minimum leader headway settings.

Professor Bekhor posed questions that are "How will autonomous vehicles and traditional ones coexist?" and "How will the introduction of autonomous vehicles affect travel habits in general and the mode choice in particular?" These questions include whether AVs will induce people to travel more and whether they will benefit. Each intermediate scenario which is the result of introducing AVs can be seen as a parametrized combination of User Equilibrium (UE) and System Optimum (SO). He proposed the methodology using the dynamic representation of the problem. The initial calculation based on it using MSA (multi-scenario approach) showed the different tendency of moving from UE to SO in each example network.

Mr. Hayakawa showed the traffic situations with AVs in which there are complicated interactions between the administrator, service operators, and customers. Then, he focussed on the sequential mechanism for a service operator that aims to maximize social welfare. There, the objective is to maximize social welfare which is defined as the summation of the users’ utility based on Activity model under time-space constraints of users and under capacity constraints of the road network. To solve the problem, he used ZDD (Zero-suppressed binary decision diagram) and MSA. In numerical study, he showed that his proposed mechanism can remain high efficiency when increasing the number of agents. Finally, he suggested that his mechanism can be easily extended to an auction-based implementation by which the social optimal states are achieved.

Ms. Hara introduced "Easy Ride" which is the autonomous drive vehicles service developed by Nissan & DeNA. This autonomous drive vehicles are set very accurate digital map and detect signals and obstacles. It enables users to move to where they want to go with the application. Before the field test at Minatomirai area in Yokohama city where there are many kinds of people (residents, tourist, and worker), she checked the market volume in this area using Person Trip data and the SP survey they conducted. She pointed out that this result is static and what they want to know is the demand changing dynamically. Then, how to manage and how to calculate the demand dynamically are very big issues. And social understanding is also a big issue.

Mr. Parady introduced his research “THE EFFECT OF SEAWALLS ON TSUNAMI EVACUATION DEPARTURE TIME: A CASE STUDY OF THE 2011 GREAT EAST JAPAN EARTHQUAKE”. Using data from a survey of survivors of the 2011 Great East Japan Earthquake and Tsunami, he evaluated quantitatively the effect of seawalls on evacuation departure time. A mixed effect Cox proportional hazard model was estimated, using time-dependent covariates to account for the changing nature of tsunami risk. Findings suggest that on average, the presence of an effective seawall (defined as a seawall higher than the forecast tsunami height at any given time) delays evacuation by 29.8%. Furthermore, irrespective of forecast tsunami height, higher seawalls were associated with 11.6% and 15.2% delays in departures for walls of 4 to 8 meters and over 8 meters, respectively. These results provide quantitative evidence of the existence of a false sense of security deriving from the presence of seawalls, a hypothesis that to the best of the authors’ knowledge has only been discussed qualitatively. Given that timely evacuation is one of the main factors affecting survival probability, the effect of seawalls on evacuation delay or non-evacuation is an important policy consideration.

Mr. Urata introduced an optimization approach for the dynamic strategy of evacuation and picking-up behavior to respond to tsunami risk. He started by suggesting that preparation and “pick-up” behavior is an important factor of evacuation delay in the 2011 Great East Japan Earthquake. His research purpose is to propose an optimal dynamic strategy for reducing the evacuees’ risk with a pick-up behavior. Assuming that a liner city and simple network, he estimated the risk evaluation, He showed dynamic control for picking-up behavior on bottleneck intersection. Because of some people who take a risk and pick up their family members, congestion can occur. A deductive and sequential approach to reach an optimal dynamic strategy with a low computational cost was proposed. He indicated that using simple control approach which shows just a stop timing, evacuees can understand easily.

As a recent research, Mr. Yaginuma proposed a road evaluation method in the case of a disaster. He began with introducing that earthquakes can occur in various places in Japan, participants from Israel were very surprised that disasters were predicted over a wide area. Cost by Benefit evaluation norm is usually used in Japan, but this norm considers only normal everyday life and the emergency road vulnerability is ignored. He insisted that the evaluation of connectability is important and developed two new models. He calculated the log sum variable for the OD pair, calculated travel time, and evaluated road network. First of all, as a simple case, he showed an example result assuming that the vulnerability of the road is distributed. Next, taking into consideration the shutdown risk of roads and bridges endangered by Tsunami or other disasters, he applied it to actual highway network and showed the result.

Mr. Yamano talked about Probe Person (PP) technique using GPS. He showed an analysis about the walking speed based on PP technique. And then, he introduced new approach of travel behavior analysis based on information theory using sensor data and PP data simultaneously. He showed the experiment of OD demand estimation and suggests the difference between including error and error-free in measurements. His model will be able to take into account various error in measurements, sampling, and estimation.

His presentation theme was “Network Design Problem considering system time minimization and road safety maximization”. For network design, various kinds of problems need to be solved. We decide the objective function on upper level considering and the user equilibrium on a lower level. Also, we have to estimate the system time of different project combinations. He presented a solution for the multi-objective NDP. The objective criteria were minimizing the system time and maximizing the safety in the network. The model was applied for a large set of candidate projects on a real-size network, which demonstrates its ability to provide solutions for real problems. Since the crash prediction model is location-specific, implementation of the model on different networks will necessitate the use of alternative CPM. In order to find the Pareto front, the multi-objective optimization NSGA-II was used. No major differences were found using different population sizes.

Mr. Toledo proposed real-time control for transit systems with transfers. These days, because integrated public transportation systems improve delay, new real-time operations control is required. The road was assumed to be under single line control or multiple lines control. In each case, he showed the result of the case study. He developed a system for real-time transit control based on a prediction. In multiple line case, he set optimization problem to investigate the total delay in the system. He assumed the speed and holding time constraints. Considering holding and speed change, this approach accounts for transfers and vehicle capacities. Moreover, the transit can be evaluated as on-going against no and headway control. As next steps, he was trying to improve the predictions and the robustness.

He introduced his research about methods for demand transportation and public transport (bus) operation plan. In order to enumerate solution patterns to the demand plan of the demand transport, he proposed a method using ZDD. First, he expressed the operation route of demand traffic by a graph structure and enumerated all subgraph structures satisfying certain constraints from the given graph structure. Next, using compressed ZDD structure, it is possible to get not only the best solution but also other good solution. In the traditional method, only the best solution was obtained. However, in the actual planning, the best solution may be unable to run. Therefore, he suggested that this approach would be useful for traffic planning under such circumstances. The usefulness was confirmed in a case study. In the bus operation plan, we have to consider fairness and economy of the plan together. He proposed an evaluation model that quantifies the potential public transportation demand by sub-district within a municipality based on DEA and clarifies concrete benchmarks in the district. In addition, he applied the developed model to the actual city and examined the district to focus on promoting the use of public transportation.