Last-mile Travel Research Articles

Comparison of the Workday and Non-Workday Carbon Emission Reduction Benefits of Bikeshare as a Feeder Mode of Metro Stations

Bikeshare, as a convenient transport mode, can address the first- and last-mile travel needs of metro trips while generating many environmental benefits, such as reducing the use of environmentally unfriendly transport modes and lowering the carbon emissions of the urban transportation system. This paper takes bikeshare as a feeder mode of metro stations (BS-FMMS) as the research object and compares the spatial and temporal differences in the carbon emission reduction benefits of BS-FMMS on workdays and non-workdays by using the framework of BS-FMMS carbon reduction benefit analysis and the methods of time-series analysis, spatial aggregation analysis, and box plot analysis. The results show that the carbon emission reduction benefit of bikeshare has obvious morning and evening peaks on workdays, while it tends to be stable without obvious peaks during the day on non-workdays. From the perspective of spatial distribution, the carbon emission reduction benefits of BS-FMMS are more significant in the metro station areas in the south of Baoan district, the west of Nanshan district, the central of Longhua district, and the south of Futian district in Shenzhen city, and the metro stations where the carbon emission reduction benefits of the non-workday are greater than those of the workday are mainly concentrated in Nanshan district, Futian district, and Luohu district. There is a significant positive correlation between BS-FMMS ridership and carbon emission reduction. These findings can provide clear policy implications for the decarbonization development of urban transportation systems.

Comparing the bad media-fuelled reputation of e-scooters with real-life user and non-user perceptions: Evidence from Sweden

E-scooters, one of the most rapidly growing forms of micromobility globally, entered our cities as an innovative first and last-mile travel mode looking to complement public transit and reduce short car trips. However, there are many voices openly suggesting that e-scooters, despite representing still a very low modal share, have instead been the source of traffic accidents, transport system disruption and public space anarchy. Despite a wealth of e-scooter research appearing lately, knowledge gaps about e-scooters’ current reputation and how this matches actual user and non-user perceptions do exist. Our two-phase mixed method approach means to fill in these gaps by examining the intriguing context of Sweden, a country hosting more than 30 million e-scooter trips on an annual basis but recently enforcing new stricter rules for their regulation. This paper contributes to the state of the art by contextualising the current reputation of e-scooters in Sweden through a discourse analysis of local press items and by analysing an attitudinal survey that ran in Stockholm and Gothenburg with almost equal numbers of e-scooter users and non-users. First our news item analysis identifies six reputation-defining themes: cityscape fit; traffic safety and irresponsible user behaviour; rules, regulations and exploits; business; sustainability; and convenience and concludes that e-scooters’ portrayal in Sweden is very negative. There are approximately five negative comments in the press for every positive one. Our survey offers statistical evidence that there are significant differences in the evaluations of users and non-users when it comes to seven perceived e-scooter qualities (safety; speed; eco-friendliness; cost; convenience; fun; health and wellbeing) and two policy practicalities (regulations clarity; and parking provision). Users’ perceptions are always more positive from those of non-users, but they do agree with them that safety, eco-friendliness, cost, health and wellbeing are areas of concern for e-scooters. Both groups see value in enhanced regulation clarity and better parking provision. Non-users however, to some degree, do recognise that e-scooters offer fast, convenient and pleasant mobility services making this the tripole where image change and positive rebranding could start from.

On demand ride sharing: Scheduling of an autonomous bus fleet for last mile travel

Autonomous buses are expected to expand the mobility-on-demand options in cities in the next 10 years. An essential aspect of ensuring optimal usage of fleets of autonomous buses is the task of scheduling. This paper presents a scheduling approach based on the construction of all possible trips used to formulate an optimization problem. While in the past most research is focused on the scheduling of taxi trips, there is an increasing interest in the research for the scheduling of last-mile travel options. A street network has been generated based on open street map data as a basis for the scheduling. All possible combinations of buses and requests are calculated, and for each of those trips, a close to optimal order of requests is created. These are used to formulate the optimization problem, calculating a close to optimal assignment of requests on the buses. The approach has considered constraints such as maximum waiting time, travel delay, and targets to exploit shared trips for higher efficiency. Experiments have been carried out in a simulated environment of a university campus area with fleets of up to 10 vehicles. By performing various trials with changing parameters, the influence of the constraints on waiting time and travel delay to the scheduling is determined. Depending on the setup, service rates above 90%, while trails with strict constraints show that the approach can handle short-term requests. Based on the results, a use case-specific composition of the autonomous bus fleet can be done.

Shared Autonomous Vehicles as Last-Mile Public Transport of Metro Trips

The “last-mile problem” of public transportation is one of the main obstacles affecting travelers who choose to utilize public transport. Although autonomous vehicles (AVs) have made much progress, they have not been officially put into commercial use. This paper adopts stated preference experiments to explore the impact of shared AVs on the last-mile travel behavior of metro users and takes Wuhan as an example for case analysis. First of all, this paper establishes a structural equation model (SEM) based on the theory of planned behavior to explore latent psychological variables, including travelers’ attitudes (ATTs), subjective norms (SNs), perceived behavior control (PBC), and behavioral intention of use (BIU) toward AVs. These latent psychological variables are incorporated into the latent class (LC) logit model to establish a hybrid model with which to study the factors and degree of influence on the travel mode choices of travelers for the last mile of their metro trips. The results show that travelers have preference heterogeneity for the travel mode choices for the last mile of metro trips. Through the analysis of LCs, education, career, and income significantly impact the classification of LCs. The latent psychological variables towards AVs have a significant impact on the travel behavior of respondents, but the impacts vary among different segments. Elastic analysis results illustrate that a 1% increase in the travel cost for shared AVs in segment 1 leads to a 7.598% decrease in the choice probability of using a shared AV. Respondents from different segments vary significantly in their willingness to pay for their usage, and the value of travel time for high-income groups is relatively higher.

Micromobility Vehicles in India: Urban Commute Analysis with users’ Perspective

Since 1817 there has been using of the term micro-mobility from bicycles to modern-day electric-powered micro-mobility vehicles. The paper presents the current Indian urban commute, user behaviour, Indian urban scenario of last and first-mile transportation and scope of micro-mobility vehicles in Indian urban markets for small distance commutes. This also discusses the potential challenges in the growth of the micro-mobility ecosystem. A survey is conducted locally for urban cases and presented to take users' perspectives and preferences regarding daily commute and last-mile travel options.
 Keywords: Charging facilities, Electric Vehicles, e-PMV, Last Mile Transport, Micro mobility, Network, Sustainable transport, Startups, Traffic Congestion, Urban Areas

Integrating shared autonomous vehicles in Last-Mile public transportation

While public transportation (PT) plays a critical role in urban mobility, the discomfort and the disutility of the last-mile trips make PT unattractive. We study the feasibility of shared autonomous vehicles (AV) as an on-demand alternative to fixed-route buses for last-mile travel. To this end, the bus routes that operate along the edges of the PT network in Bursa, Turkey are selected. The origin and destination (OD) pairs of the trips made on these bus routes are inferred at an individual level. The vehicle routing problem is then formulated by considering the proposed transportation service. Several solution methods are developed for both solution construction and solution improvement phases of the problem. To assess solution methods with real-world data, an agent-based simulation framework is built. It is found that the solution methods successfullysolved a dynamic problem. The results show that the integration of PT using AVs is well-suited to improve the service quality and energy efficiency in the last-mile mobility.

ParcEMon: IoT Platform for Real-Time Parcel Level Last-Mile Delivery Greenhouse Gas Emissions Reporting and Management.

Transport is Australia’s third-largest source of greenhouse gases accounting for around 17% of emissions. In recent times, and particularly as a result of the global pandemic, the rapid growth within the e-commerce sector has contributed to last-mile delivery becoming one of the main emission sources. Delivery vehicles operating at the last-mile travel long routes to deliver to customers an array of consignment parcels in varying numbers and weights, and therefore these vehicles play a major role in increasing emissions and air pollutants. The work reported in this paper aims to address these challenges by developing an IoT platform to measure and report on real-world last-mile delivery emissions. Such evaluations help to understand the factors contributing to freight emissions so that appropriate mitigation measures are implemented. Unlike previous research that was completed in controlled laboratory settings, the data collected in this research were from a delivery vehicle under real-world traffic and driving conditions. The IoT platform was tested to provide contextualised reporting by taking into account three main contexts including vehicle, environment and driving behaviours. This approach to data collection enabled the analysis of parcel level emissions and correlation of the vehicle characteristics, road conditions, ambient temperature and other environmental factors and driving behaviour that have an impact on emissions. The raw data collected from the sensors were analysed in real-time in the IoT platform, and the results showed a trade-off between parcel weight and total distance travelled which must be considered when selecting the best delivery order for reducing emissions. Overall, the study demonstrated the feasibility of the IoT platform in collecting the desired levels of data and providing detailed analysis of emissions at the parcel level. This type of micro-level understanding provides an important knowledge base for the enhancement of delivery processes and reduction of last-mile delivery emissions.

Investigation of shared micromobility preference for last-mile travel on shared parking lots in city center

This study explores travelers’ preference for shared micromobility in the context of last-mile travel from shared parking lots to their final destination in the city center. Based on the results of the stated preference experiment and error component logit model incorporating latent variables, we found that the preference for shared micromobility is affected by the uncertainty of availability as well as both travel time and fare. More specifically, our alternative-specific part-worth utility approach shows that their marginal effects are non-linear, and this suggests several policy implications. Also, we found that there is a significant substitution between shared micromobility modes. Furthermore, various latent perspectives, including safety and management concerns of shared micromobility, have significant impacts on the preference.

Connecting metros with shared electric scooters: Comparisons with shared bikes and taxis

The rapid rise of shared electric scooter (E-Scooter) systems offers urban areas a new micro-mobility solution. The focus on short-distance travel has made it a competitive option for addressing first-/last-mile travel needs. Nevertheless, its role as a first-/last-mile solution was understudied due to the lack of fine-grained trip data. This study aims at exploring the integration of shared E-Scooters with public transportation systems. Specifically, it compared the use of shared E-Scooters against shared bikes and taxis for connecting trips from/to metro stations. We analyzed massive amounts of trip-related data extracted through APIs. Multinomial logistic regression models were developed to uncover how the mode choices from/to metro stations vary in different contexts. The results show that the use of shared E-Scooters to connect trips from/to metro stations can be notably different from shared bikes and taxis. The preference of shared E-Scooters will vary depending on the land use and time period.

FACILITATING MICROMOBILITY FOR FIRST AND LAST MILE CONNECTION WITH PUBLIC TRANSIT THROUGH ENVIRONMENTAL DESIGN: A CASE STUDY OF CALIFORNIA BAY AREA RAPID TRANSIT STATIONS

AbstractMicromobility has the potential to reduce greenhouse gas emissions, traffic congestion, and air pollution, particularly when replacing private vehicle use in conjunction with public transit for first- and last-mile travel. The design of the built environment in and around public transit stations plays a key role in the integration of public transit and micromobility. This research presents a case study of rail stations in the California Bay Area, which are in the operation zone of seven shared micromobility operators. Nineteen stations and their surroundings were surveyed to inventory design features that could enable or constrain use of micromobility for first- and last-mile access. Shared mobility service characteristics, crime records, and connections to underserved communities were also documented. Key design solutions were identified based on the findings, including protected bike lanes, increased shared bike and scooter fleet size and service area, and clear signage indicating parking corral and docking points.

Estimating the Potential for Shared Autonomous Scooters

Recent technological developments have shown significant potential for transforming urban mobility. Considering first- and last-mile travel and short trips, the rapid adoption of dockless bike-share systems showed the possibility of disruptive change, while simultaneously presenting new challenges, such as fleet management or the use of public spaces. In this paper, we evaluate the operational characteristics of a new class of shared vehicles that are being actively developed in the industry: scooters with self-repositioning capabilities. We do this by adapting the methodology of shareability networks to a large-scale dataset of dockless bike-share usage, giving us estimates of ideal fleet size under varying assumptions of fleet operations. We show that the availability of self-repositioning capabilities can help achieve up to 10 times higher utilization of vehicles than possible in current bike-share systems. We show that actual benefits will highly depend on the availability of dedicated infrastructure, a key issue for scooter and bicycle use. Based on our results, we envision that technological advances can present an opportunity to rethink urban infrastructures and how transportation can be effectively organized in cities.

Traffic Safety Perception, Attitude, and Feeder Mode Choice of Metro Commute: Evidence from Shenzhen.

Like many other transit modes, the metro provides stop-to-stop services rather than door-to-door services, so its use undeniably involves first- and last-mile issues. Understanding the determinants of the first- and last-mile mode choice is essential. Existing literature, however, mostly overlooks the mode choice effects of traffic safety perception and attitudes toward the mode. To this end, based on a face-to-face questionnaire survey in Shenzhen, China, this study uses the two-sample t-test to confirm the systematic differences in traffic safety perception and attitudes between different subgroups and develops a series of multinomial logistic (MNL) models to identify the determinants of first- and last-mile mode choice for metro commuters. The results of this study show that: (1) Walking is the most frequently used travel mode, followed by dockless bike-sharing (DBS) and buses; (2) Variances in traffic safety perception and attitude exist across gender and location; (3) Vehicle-related crash risks discourage metro commuters from walking to/from the metro station but encourage them to use DBS and buses as feeder modes; (4) DBS–metro integration is encouraged by the attitude that DBS is quicker than buses and walking, and positive attitudes toward the bus and DBS availability are decisive for the bus–metro and DBS–metro integration, respectively; and (5) Substantial differences exist in the mode choice effects of traffic safety perception and attitudes for access and egress trips. This study provides a valuable reference for metro commuters’ first- and last-mile travel mode choice, contributing to developing a sustainable urban transport system.

Comparative Greenhouse Gas Footprinting of Online versus Traditional Shopping for Fast-Moving Consumer Goods: A Stochastic Approach.

Variability in consumer practices and choices is typically not addressed in comparisons of environmental impacts of traditional shopping and e-commerce. Here, we developed a stochastic model to quantify the variability in the greenhouse gas (GHG) footprints of product distribution and purchase of fast-moving consumer goods (FMCGs) via three prevalent retail channels in the United Kingdom (U.K.). We found that shopping via bricks and clicks (click and fulfillment via physical store delivery) most likely decreases the GHG footprints when substituting traditional shopping, while FMCGs purchased through pure players with parcel delivery often have higher GHG footprints compared to those purchased via traditional retail. The number of items purchased and the last-mile travel distance are the dominant contributors to the variability in the GHG footprints of all three retail channels. We further showed that substituting delivery vans with electric cargo bikes can lead to a GHG emission reduction of 26% via parcel delivery. Finally, we showed the differences in the “last mile” GHG footprint of traditional shopping in the U.K. compared to three other countries (China, Netherlands, and the United States), which are primarily caused by the different shares of modes of transport (walking and by car, bus, and bike).

Locating collection and delivery points for goods’ last-mile travel: A case study in New Zealand

Collection and Delivery Points (CDPs) have been proved to be efficient in terms of decreasing the occurrence of first-time failed deliveries and delivery costs of goods bought online in European countries. However, CDPs have only recently been introduced in New Zealand, and service providers are trialling with a few CDPs established in larger cities, such as Auckland, Wellington and Christchurch. Selection of CDP locations on an ad-hoc basis is likely to be expensive for service providers and the adopted location configuration may not be able to attract the desired consumer patronage. Thus, selection of locations and the density of CDPs needs to be done on the basis of considering factors, such as consumers’ distribution pattern and accessibility to potential CDP locations. This study aimed to identify the optimal density and locations for establishing CDPs, using Christchurch as a case study. A set-covering problem approach to locating facilities was adopted, taking city demographics and travel distances between population centres and potential facility locations into consideration. Non-conventional CDPs, such as dairies and supermarkets were found to be more accessible, and thus may well prove to be efficient for locating CDPs than traditional post shops. The results also suggest the spatial location of facilities forming an optimal configuration of CDPs.

Last-Mile Travel Mode Choice: Data-Mining Hybrid with Multiple Attribute Decision Making

Transit offers stop-to-stop services rather than door-to-door services. The trip from a transit hub to the final destination is often entitled as the “last-mile” trip. This study innovatively proposes a hybrid approach by combining the data mining technique and multiple attribute decision making to identify the optimal travel mode for last-mile, in which the data mining technique is applied in order to objectively determine the weights. Four last-mile travel modes, including walking, bike-sharing, community bus, and on-demand ride-sharing service, are ranked based upon three evaluation criteria: travel time, monetary cost, and environmental performance. The selection of last-mile trip modes in Chengdu, China, is taken as a typical case example, to demonstrate the application of the proposed approach. Results show that the optimal travel mode highly varies by the distance of the “last-mile” and that bike-sharing serves as the optimal travel mode if the last-mile distance is no more than 3 km, whilst the community bus becomes the optimal mode if the distance equals 4 and 5 km. It is expected that this study offers an evidence-based approach to help select the reasonable last-mile travel mode and provides insights into developing a sustainable urban transport system.

Impact of Built Environment on First- and Last-Mile Travel Mode Choice

The paper studies the impacts of built environment (BE) on the first- and last-mile travel modal choice. We select Singapore as a case study. The data used for this work is extracted from the first- and last-mile trips to mass rapid transit (MRT) stations in the Household Interview Travel Survey of Singapore in 2012 with nearly 24,000 samples. The BE indicators are quantified based on four “D” variables: Density, Diversity, Design, and Distance to transit. We also take into account sociodemographic and trip-specific variables. Mixed logit (ML) modeling frameworks are adopted to estimate the impact of BE and the heterogeneity of taste across the sample. Based on the availability of light rail transit (LRT) in different areas, two modeling structures are implemented with binary ML models for non-LRT areas where “walk” and “bus” are the available travel modes, and multinomial ML models for areas where LRT is an additional alternative. The modeling results shed light on the following findings: BE—especially distance to MRT station, transportation infrastructures, land-use mix, and socioeconomic activities—significantly influences the first- and last-mile travel behaviors. Those who live or work close to MRT stations and in an area with high socioeconomic activities and land-use mix may have stronger preferences to walk for the first- and last-mile trips. The impact of physical BE (i.e., distance, infrastructures) is relatively homogeneous among the sample, while the impact of socioeconomic BE factors (i.e., floor space density, entropy) tends to vary across the sample.

Last-mile travel and bicycle sharing system in small/medium sized cities: user’s preferences investigation using hybrid choice model

First and last-mile access to and from public transport stations/stops is a major problem for encouraging public transport use. Bicycle sharing schemes have shown potential to fill this gap. Consequently, railway operators in the Netherland and Germany have started their own bike sharing schemes. Majority of the studies examined the preferences for using bike share schemes for larger cities. This study analyses the collected stated preference survey data for the use of bicycle sharing scheme for last mile travel, which is recently launched in small/medium sized cities of Belgium. Within this scheme a single docking station is available and users need to return bicycle at the same station. The survey also includes questions on respondents attitudes towards friendliness-to-cycling. The hybrid choice modelling framework is used to investigate preferences of users. Usual explanatory variables such as temperature, rain conditions, distance, rental cost, gender and age are found significant, which confirms the findings of earlier studies. Along with these; last-mile (to home) and an interaction term between rental cost and duration to keep bicycle are found significant, which indicate the negative effects of having a single docking station. Availability of escorting facility from parents/colleague/friends (a more common phenomenon in small/medium cities) also has a negative effect on the use of the examined bike share scheme. Paper also discusses promotional campaigns and marketing efforts according to the obtained results for making such schemes more successful.