Exploring for Route Preferences of Subway Passengers Using Smart Card and Train Log Data

As the mode share of the subway in Seoul has increased, the estimation of passenger travel routes has become a crucial issue to identify the congestion sections in the subway network. This paper aims to estimate the travel train of subway passengers in Seoul. The alternative routes are generated based on the train log data. The travel route is then estimated by the empirical cumulative distribution functions (ECDFs) of access time, egress time, and transfer time. The train choice probability is estimated for alternative train combinations and the train combination with the highest probability is assigned to the subway passenger. The estimated result is validated using the transfer gate data which are recorded on private subway lines. The result showed that the accuracy of the estimated travel train is shown to be 95.6%. The choice ratios for no-transfer, one-transfer, two-transfer, three-transfer, and four-transfer trips are estimated to be 53.9%, 37.7%, 6.5%, 1.5%, and 0.4%, respectively. Regarding the practical application, the passenger kilometers by lines are estimated with the travel route estimation of the whole network. As results of the passenger kilometer calculation, the passenger kilometer of the proposed algorithm is estimated to be 88,314 million passenger kilometer. The proposed algorithm estimates the passenger kilometer about 13% higher than the shortest path algorithm. This result implies that the passengers do not always prefer the shortest path and detour about 13% for their convenience.

The Impact of Fleet Coordination on Taxi Operations

On-demand mobility has existed for more than 100 years in the form of taxi systems. Comparatively recently, ride-hailing schemes have also grown to a significant mode share. Most types of such one-way mobility-on-demand systems allow drivers taking independent decisions. These systems are not or only partially coordinated. In a different operating mode, all decisions are coordinated by the operator, allowing for the optimization of certain metrics. Such a coordinated operation is also implied if human-driven vehicles are replaced by self-driving cars. This work quantifies the service quality and efficiency improvements resulting from the coordination of taxi fleets. Results based on high-fidelity transportation simulations and data sets of existing taxi systems are presented for the cities of San Francisco, Chicago, and Zurich. They show that fleet coordination can strongly improve the efficiency and service level of existing systems. Depending on the operator and the city’s preferences, empty vehicle distance driven and fleet sizes could be substantially reduced, or the wait times could be reduced while maintaining the current fleet sizes. The study provides clear evidence that full fleet coordination should be implemented in existing mobility-on-demand systems, even before the availability of self-driving cars.

Preference-Based Multi-Objective Optimization for Synchromodal Transport Using Adaptive Large Neighborhood Search

Decision-makers in synchromodal transport (ST) have different preferences toward different objectives, such as cost, time, and emissions. To solve the conflicts among objectives and obtain preferred solutions, a preference-based multi-objective optimization model is developed. In ST, containers need to be transferred across modes, therefore the optimization problem is formulated as a pickup and delivery problem with transshipment. The preferences of decision-makers are usually expressed in linguistic terms, so weight intervals, that is, minimum and maximum weights, are assigned to objectives to represent such vague preferences. An adaptive large neighborhood search is developed and used to obtain non-dominated solutions to construct the Pareto frontier. Moreover, synchronization is an important feature of ST and it makes available resources fully utilized. Therefore, four synchronization cases are identified and studied to make outgoing vehicles cooperate with changes of incoming vehicles’ schedules at transshipment terminals. Case studies in the Rhine-Alpine corridor are designed and the results show that the proposed approach provides non-dominated solutions which are in line with preferences. Moreover, the mode share under different preferences is analyzed, which signals that different sustainability policies in transportation will influence the mode share.

Immigrant settlement patterns, transit accessibility, and transit use

Public transit is immensely important among recent immigrants for enabling daily travel and activity participation. The objectives of this study are to examine whether immigrants settle in areas of high or low transit accessibility and how this affects transit mode share. This is analyzed via a novel comparison of two gateway cities: Sydney, Australia and Toronto, Canada. We find that in both cities, recent immigrants have greater levels of public transit accessibility to jobs, on average, than the overall population, but the geography of immigrant settlement is more suburbanized and less clustered around commuter rail in Toronto than in Sydney. Using logistic regression models with spatial filters, we find significant positive relationships between immigrant settlement patterns and transit mode share for commuting trips, after controlling for transit accessibility and other socio-economic factors, indicating an increased reliance on public transit by recent immigrants. Importantly, via a sensitivity analysis, we find that these effects are greatest in peripheral suburbs and rural areas, indicating that recent immigrants in these areas have more risks of transport-related social exclusion due to reliance on insufficient transit service.

Spatial variation in bicycling: a retrospective review of travel survey data from Greater Melbourne, Australia

Understanding spatial variation in bicycling within cities is necessary to identify and address inequities. We aimed to explore spatial variation in bicycling and explore how bicycling rates vary across population sub-groups. We conducted a retrospective analysis of household travel survey data in Greater Melbourne, Australia. We present a descriptive analysis of bicycling behaviour across local government areas (LGAs; n=31), with a focus on quantifying spatial variation in the number and proportion of trips made by bike, and by age, sex and trip distance. Associations between the proportion of infrastructure that had provision for biking and the proportion of all trips made by bike were analysed using linear regression. Overall, 1.7% of all trips were made by bike. While more than half (53.2%) of all trips were less than 5km, only 2% of these trips were by bike. Across LGAs, there was considerable variation in the proportion of trips made by bike (range: 0.1% to 5.7%). Mode share by females was 35.0%, and this varied across LGAs from 0% to 49%. Tor each percentage increase in the proportion of infrastructure that had provision for biking, there was an associated 0.2% increase in the proportion of trips made by bike (coefficient = 0.20; SE = 0.05; adjusted R2 = 0.38). While we observed a low bicycle mode share, more than half of all trips were less than 5 km, demonstrating substantial opportunity to increase the number of trips taken by bike.

Evaluating the Capacity Coordination in the Urban Multimodal Transport Network

The urban multimodal transport network is composed of multiple layers of networks; thus, coordinating the capacity equilibrium among different sub-transport networks plays a crucial role to keep the entire network running efficiently. To quantify and evaluate the passenger flow distribution in an urban multimodal transport network, this research proposes a method to evaluate the capacity coordination in an urban multimodal transport network on the basis of assignment results calculated by the Stochastic User Equilibrium (SUE) model considering the link and path impedance of different sub-transport networks. It suggests evaluation functions for the indicator level of service (LOS) of the multimodal transport network, Gini coefficient of transport network, and mode share of transport modes, and it shows how the functions were estimated. Then, it reports on results with the evaluation scheme collected in a multimodal example application for roadway network, transit networks (bus transit network and urban rail transit network), and connection network. The evaluation results under different assumed origin–destination (OD) demand show the coordination degree and can be used to recognize shortcomings of the network. Moreover, the OD demand interval of real network with good coordination can be deduced, which can also help transport planners to find the optimal strategy.

Mitigating Increased Driving after the COVID-19 Pandemic: An Analysis on Mode Share, Travel Demand, and Public Transport Capacity

Reduced transit capacity to accommodate social distancing during the COVID-19 pandemic was a sudden constraint that along with a large reduction in total travel volume and a shift in activity patterns contributed to abrupt changes in transportation mode shares across cities worldwide. There are major concerns that as the total travel demand rises back toward prepandemic levels, the overall transport system capacity with transit constraints will be insufficient for the increasing demand. This paper uses city-level scenario analysis to examine the potential increase in post-COVID-19 car use and the feasibility of shifting to active transportation, based on prepandemic mode shares and varying levels of reduction in transit capacity. An application of the analysis to a sample of cities in Europe and North America is presented. Mitigating an increase in driving requires a substantial increase in active transportation mode share, particularly in cities with high pre-COVID-19 transit ridership; however, such a shift may be possible based on the high percentage of short-distance motorized trips. The results highlight the importance of making active transportation attractive and reinforce the value of multimodal transportation systems as a strategy for urban resilience. This paper provides a strategic planning tool for policy makers facing challenging transportation system decisions in the aftermath of the COVID-19 pandemic.

Promoting Bicycling in Car-Oriented Cities: Lessons from Washington, DC and Frankfurt Am Main, Germany

This paper compares bicycling in Washington, DC and Frankfurt am Main, Germany, two car-oriented cities that had adapted their urban transport system to car travel during the 20th century. Our comparative case study shows that both cities have been successful in increasing the percentage of trips made by bicycle between the late 1990s and 2018: Washington, DC from 1% to 5% and Frankfurt from 6% to 20% of trips. Both cities had detailed bike plans and specific mode share goals for bicycling. However, those plans were only used as guideposts for a step-by-step approach to bicycle promotion that focused on integrating bicycling into everyday decision making in transport, traffic engineering, and urban development. This step-by-step approach successfully garnered political, public, and administrative support over time. The downside of this incrementalist approach is that bike route networks in both cities still have many gaps because bikeway infrastructure was built when individual opportunities arose and not as part of an integrated network. Bicycle promotion in both cities used a combination of bikeway infrastructure and soft policy, including marketing measures. In both cities, the quality of newly installed bikeway infrastructure increased over time from simple bike lanes to protected bike lanes separating cyclists from traffic. In contrast to Washington, DC, Frankfurt has a longer history of car-restrictive policies and overall has been more strict in limiting car use.