Dynamic vehicle routing problem for flexible buses considering stochastic requests

Flexible buses provide on-demand services to one or more local communities in a specific geographical area. Bus routes can be adjusted dynamically according to real-time passenger demand in a cost-effective manner. This study investigated the dynamic bus-routing problem considering stochastic future passenger demand. A two-stage stochastic programming model was formulated to minimise the total vehicle travel time cost and penalty for rejecting requests. A rolling horizon scheme was adopted to handle the dynamic changes in passenger requests and vehicle routes. A vector-similarity-based clustering and adaptive large neighbourhood searching (VSC-ALNS) algorithm was developed to solve this problem. Vehicles and passengers were matched and clustered into groups based on vector similarity, and vehicle routes were generated using an adaptive large-neighbourhood search algorithm for each cluster. The effectiveness of the proposed method was evaluated in four cases with different demand intensities using Shanghai taxi order data. The results indicate that flexible buses are more suitable for moderate demand cases, ranging from 20 to 50 requests per square kilometre per hour

Modeling mode choice of customized bus services with loyalty subscription schemes in multi-modal transportation networks

This study models the equilibrium mode choice of emerging customized bus (CB) services in a multi-modal urban transportation network. Two unique characteristics of CB services are considered: (1) the reservation of a seat for every CB passenger, and (2) the loyalty scheme for long-term CB subscribers. The first characteristic eliminates in-vehicle crowding but imposes tight constraints on CB demand, while the second characteristic encourages passenger loyalty to CB services. An advanced equilibrium mode choice model based on the dogit model, a well-established travel choice model that is widely used to consider captive mode choices, is proposed to account for the choice behavior of passengers loyal to CB services. In contrast, a nested weibit model is used to account for the choice behavior of passengers not loyal to CB services by simultaneously considering the effects of mode similarity and mode heterogeneity on mode choice behavior. The proposed model is formulated as an equivalent mathematical programming problem, which guarantees the existence and uniqueness of solutions. Numerical experiments are conducted to illustrate the properties and applicability of the proposed model in a real-world setting. The results demonstrate the importance of considering passenger loyalty and managing mode similarity and heterogeneity when modeling emerging CB services. The results also show the applicability of the proposed model for evaluating the performance of a network with CB services and the design and pricing of CB operation and subscription schemes

Impact of half-price public transport fares – a research note

When compared to pre-COVID (2019) periods, available data shows lower total patronage during the half-price fares period. There is evidence of long-term adjustments in travel patterns that could continue to suppress PT patronage going forward, even if fares are reduced

Pandemic impacts on public transport safety and stress perceptions in Nordic cities

COVID-19 has brought severe disruption and demand suppression to mobility, especially to public transport (PT). A key challenge now is to restore trust that PT is safe again. This paper investigates pandemic impacts on PT safety and stress perceptions in three Nordic cities, drawing on 2018 and 2020 survey data analysed in structural equation models. While finding modest pandemic effects on safety and stress perceptions overall, strong heterogeneities exist across gender, age and geographic categories. Women perceive less PT safety and more stress, especially during the pandemic. Older adults reduced PT more during the pandemic and perceived no stress reduction like younger adults. Stockholm travellers feel less safe and more stressed than in Oslo and Bergen, whilst pandemic PT use and perceived safety reductions are least in Bergen. The paper discusses the long-term implications for theory and policy across multiple mobility scenarios accounting for modal change and travel demand uncertainties

Multi-scale urban passenger transportation CO2 emission calculation platform for smart mobility management

Passenger transportation is one of the primary sources of urban carbon emissions. Travel data acquisition and appropriate emission inventory availability make estimating high-resolution urban passenger transportation carbon emissions challenging. This paper aims to establish a method to estimate and analyze urban passenger transportation carbon emissions based on sparse trip trajectory data. First, a trip chain identification and reconstruction method is proposed to extract travelers\u27 trip information from sparse trip trajectory data. Meanwhile, a city-scale trip sampling expansion method based on population and checkpoint data is proposed to estimate population movements. Second, the identified trip information (e.g., trip origin and destination, and travel modes) is used to calculate multimodal passenger transportation CO2 emissions based on a bottom-up CO2 emissions calculation approach. Third, we develop a multi-scale high-resolution transportation carbon emission calculation and monitoring platform and take the city of Hangzhou, one of China\u27s leading cities, as our case study, with around 10 million daily trips data and a quarter million road links. Five modes of passenger transportation are identified, i.e., walking, cycling, buses, metro, and cars. Hourly carbon emissions are calculated and attributed to corresponding road links, which build up passenger transportation carbon emissions from road links to region and city levels. Results show that a typical working day\u27s total passenger transportation CO2 emission is about 36,435 tonnes, equivalent to CO2 emissions from 4 million gallons of gasoline consumed. According to our analysis of the carbon emissions produced by approximately 40,000 km of roadways, urban expressways have the most hourly carbon emissions at 194 kg/(h·km). Moreover, potential applications of the developed methods and platform linking to smart mobility management (e.g., Mobility as a Service, MaaS) and how to work in tandem to support green transportation policies (e.g., green travel rewards and carbon credits in transportation) have been discussed

Trip planning for a mobility-as-a-service system: Integrating metros and shared autonomous vehicles

Mobility as a service (MaaS) integrates various transport modes into an on-demand and real-time platform, providing door-to-door service, and has received extensive attention. For MaaS, personalized trip planning is important but intractable. In this paper, we present a two-phase decision-support optimization framework for the problem of a MaaS system incorporating metros and shared autonomous vehicles (SAVs). First, a mixed integer programming model is proposed to optimize the routes of heterogeneous travelers considering five transport mode combinations, in which SAVs are regarded as not only a first- and last-mile connector to the metro but also a competitor. Next, the scheduling of SAVs and departure time of each traveler is determined with the purpose of minimizing the SAV operation cost. To apply the proposed framework to scenarios with real-time requests, we adopt the rolling horizon solution method, which includes four sub-modules. The method is evaluated on the Sioux Falls network, and the experimental results show that travelers become more sensitive to the mode choice as the additional time of the metro increases. In addition, the connectivity of the metro network has a considerable influence on the relationship between the metro and SAVs. The methodology can be useful for the trip planning of other transport mode combinations

The effects of bike-share users’ socio-demographics and trip features on the bike-transit relationships

Understanding how bike-share interacts with public transit is vital to determining the potential benefits of bike-share on the existing urban transportation system. This study examines the effects of bike-share users’ socio-demographics and trip features on whether bike-share users integrate or substitute public transit by conducting a questionnaire survey of Seoul’s bike-share users. The multinomial logistic model (MLM) was used for the statistical analysis. Our results showed that the bike-share’s trip purpose and perceived utility are significantly associated with the modal integration and substitution between bike-share and public transit. In particular, bike-share users are more likely to integrate with public transit when they make utilitarian trips near public transit stations. Furthermore, those who substitute public transit intended to save travel costs and exercise. The study’s findings can be utilized for establishing strategies to maximize the utility of bike-share in conjunction with the public transit system

Achieving urban net-zero targets through regionalized electric bus penetration and energy transition

The electrification of public transit is one of the key actions in the transportation sector. This study proposed an innovative framework for analyzing the effectiveness and emission reduction potential of electrifying transit policies. The future energy consumption, GHG emissions, and pollutant emissions of bus fleets in representative Canadian cities were analyzed. Under the high oil price scenarios, this study incorporated the upfront infrastructure costs, the social costs of pollution, and the dynamics of carbon prices and fuel prices, allowing for a comprehensive analysis of carbon reduction costs during transition. Compared to the BAU scenarios, the cumulative GHG emissions from 2019 to 2030 of bus fleet in ESD scenarios in Toronto, Montreal, Edmonton, and Halifax had a reduction of 18.7 %, 30.1 %, 21.3 % and 34.6 %, respectively. The findings have implications for the trade-off management of climate policies at the provincial level and can help understand polycentric governance from multiple resource perspectives

Low-emission zones, modes of transport and house prices: evidence from Berlin’s commuter belt

We study the impact of a tightening of a private driving restriction in Germany’s capital, the city of Berlin, on house prices in its affluent suburbs. Using geo-referenced data on train stations, motorway access points and offers of single-family houses for sale from Germany’s leading online property broker ImmobilienScout24 in a spatially staggered DiD framework, we find evidence for sizeable price growth premia for houses located in walking distance of train stations that lie within 30 min commuting duration to Berlin main station. Property located in immediate vicinity (5 min walking distance) of train stations within 30–40 min commuting duration, however, face penalties. Our findings are of relevance for the design of public infrastructure planning policies that seek to accommodate and facilitate changes in local demands for alternative and more environmentally sustainable modes of transport induced by private driving restrictions

PM emissions from railways – Results of tests on a wheel-rail test bench

The sources of non-exhaust emissions from railways have been identified in the past. However, the quantification of the emission from railways is subject to a large degree of uncertainties, as published emission factors vary by orders of magnitude. The presented study aims a detailed quantification of the wheel-rail contact. For this reason, a series of tests at a wheel-rail test bench was conducted. The emission monitoring covered particle mass, particle size distribution and filter sampling in order to enable a chemical analysis of the particle composition. According to the test results the cumulative PM10 emissions of the wheel-rail contact (entire train) range from 0.33 to 5.57 g/km. The average proportions of PM2.5 and PM1 in the PM10 emissions were 87.5% and 68.46% respectively. While in straight-line driving and in wide curves the number of axles is the most dominant parameter, in tight curves, the bogie length becomes more relevant