Last-mile Research Articles

Beyond the last mile: different spatial strategies to integrate on-demand services into public transport in a simplified city

AbstractIntegrating on-demand services into public transport networks might be the best way to face the current situation in which these new technologies have increased congestion in most cities. When cooperating with on-demand services rather than competing with them, public transport would not risk losing users, and could attract some passengers from private modes thanks to an increased quality of service. This fact has engendered a growing literature discussing how to design such an integrated system. However, all of that research has imposed that on-demand mobility is to solve the so-called “last-mile problem”, serving only as a feeder that connects the exact origins/destinations with the traditional public transit network. As it induces a large number of transfers and it precludes some scale-effects to be triggered, in this paper we challenge that imposition and investigate if this is the best spatial integration strategy. To do so, we study a simplified linear city in a morning peak situation, where we propose seven different line structures, all of them combining a traditional fixed line with on-demand ride-pooling (ODRP): three direct structures, where ODRP can serve full trips, three semi-direct, where a single ODRP vehicle can serve the largest part of a trip, and a base case in which ODRP is restricted to the first and final legs only. Our results show that the base case is optimal only under very specific demand patterns, or when transfer penalties are disregarded. Our analytical approach reveals relevant operational aspects of such integrated systems: namely, that the base case can help increase directness (diminishing detours), and that ODRP can help shorten the routes of the fixed services to decrease operator costs.

Designing an Autonomous Mobility-on-Demand Service for Transit Last-Mile Access

Public transportation has the potential to provide a safe, convenient, affordable, and eco-friendly mobility service. However, because of its fixed routes and limited network coverage, it is sometimes difficult or impossible for passengers to walk from a transit stop to their destination. This inaccessibility problem is also known as the “transit last-mile connectivity problem.” Such a lack of connectivity can force travelers to drive, consequently increasing vehicle ownership and vehicle miles traveled (VMT) on the roads. The autonomous mobility-on-demand (AMoD) service, with characteristics such as quick fleet repositioning and demand responsiveness, has the potential to provide last-mile coverage where fixed-route transit can only provide limited coverage. This study aims to address the last-mile problem by developing an online demand-responsive AMoD service integrated with fixed-route transit. A linear time-delay dynamical system is proposed to model the AMoD system, and a model predictive control methodology is adopted to regulate the system around an equilibrium point with minimum vehicle rebalancing. To assess the impact of this new mobility service on travel demand, a simulation study is developed and integrated with a mode choice model capturing a combined transit-AMoD model. The experiment results reveal the potential to enhance transit efficiency, reduce VMT, and accommodate the increased transit demand while maintaining the quality of service.

Modulation format conversion between BPSK and 8QAM signals using coherent interference and four-wave mixing

Optical fiber networks need to transmit various types of client data with different requirements of bandwidth, reach, latency, and capacity by interconnecting backbone, metro, and local access networks. Since the optimal modulation format usually differs, it should be efficiently converted at the intermediate node connecting different networks. Among the multilevel modulation format, 8-ary quadrature amplitude modulation (8QAM) is a candidate for terrestrial networks thanks to the good balance between spectral efficiency and transmission distance. In this paper, we propose modulation format conversion systems from binary phase shift keying (BPSK) to 8QAM using coherent interference in delay interferometer and its reverse conversion system using four-wave mixing in a single highly nonlinear fiber. Conversion performances are numerically evaluated based on bit error rate, constellation diagrams, 8QAM and BPSK propagation lengths, and parameters in electrical signal regeneration. As a result, error-free format conversion is achieved for both conversion systems.

The flexible airport bus and last-mile ride-sharing problem: Math-heuristic and metaheuristic approaches

Airport buses play a crucial role in addressing the last-mile problem of air travel, especially in cities and countries lacking inner-city rail transit systems. Nevertheless, airport buses are currently witnessing a decline in ridership due to drawbacks such as long departure intervals, inflexible stops, and considerable distances between stops. Consequently, delivering high-quality airport bus services has become a pressing concern for public transport operators. Motivated by new flexible buses and ride-sharing services, this paper explores a flexible airport bus service that integrates ride-sharing services for passengers traveling from bus stops to their destinations. This problem entails integrated decisions involving bus stop selection, passenger assignment to drop-off bus stops, as well as bus and ride-sharing routing. Accordingly, this problem presents more challenges in decision-making than traditional flexible bus or ride-sharing routing problems. We first develop an arc-based mixed-integer linear programming model. Subsequently, we design a double decomposition math-heuristic algorithm that builds upon logic-based Benders decomposition and column generation algorithms to obtain a near-optimal solution within practical computation time limits for practical-scale instances. Additionally, we implement an adaptive large neighborhood search algorithm to evaluate the solution quality of this math-heuristic algorithm and to solve large-scale instances. To validate the effectiveness of both the model and the algorithms, we conduct numerical experiments using instances derived from Shenzhen airport bus lines. The experimental results demonstrate that the flexible service mode offers significant advantages in reducing both passenger ride time and vehicle mileage over traditional airport bus or taxi modes.

Revealing the driving factors and mobility patterns of bike-sharing commuting demands for integrated public transport systems

Bike-sharing for integrated public transport systems (BIPTS) offers an effective solution to the first- and last-mile problems. However, most existing studies have used overly simplified single-catchment area methods to identify BIPTS demands, and the driving factors and mobility patterns of BIPTS commuting demands have remained unclear. To fill this gap, a comprehensive framework for analyzing BIPTS commuting demands is developed. The proposed framework integrates a multi-catchment area method for precise BIPTS demands identification, the SHapley Additive exPlanations (SHAP) approach for uncovering driving factors, and a combination of dimensionality reduction and clustering techniques for discerning mobility patterns, complemented by a validation mechanism. A case study in Beijing demonstrates the efficacy of our multi-catchment areas method, which reduces misidentification of BIPTS demands by 48.6 %. Notably, for morning peak first-mile demands, the driving factors are the available bike density of cycling catchment area, the bikeability index, and the metro passenger inflow. Strong factors interactions are observed, stemming from an imbalance between BIPTS demands and infrastructure supply. Additionally, three distinct commuting patterns emerge, attributed to variations in feature contributions. These insights are crucial for enhancing the seamless integration of bike-sharing and public transport systems.

Connecting the Last Mile: The Impact of Dockless Bike-sharing on Public Transportation

In this study, we examine the impact of a new mobile-based, dockless bike-sharing service on public transportation usage. This new bike-sharing model removes the constraint of having fixed stations and gives users full flexibility on where to pick up and return bikes. This innovative feature of dockless bike sharing potentially disrupts the current norms of how people commute. The dockless shared bikes offer easy connections between destinations and public transportation stations. They can potentially promote public transportation, by improving its flexibility and outreach. To examine this impact, we collaborate with one of the largest dockless bike-sharing companies in China and collect unique daily-station-level panel data of shared-bike rides and subway traffic. Our findings indicate that a 1% increase in shared-bike rides leads to an increase of 0.35% in subway traffic. Further analyses show that this positive effect is stronger when people need to travel a longer distance to reach subway stations. These results suggest one potential underlying mechanism for the positive relationship we observe, that is, dockless shared bikes alleviate the “last-mile problem” for public transportation, making it a more appealing mode of transportation, compared with alternatives. Overall, we find that dockless shared bikes, in contrast to ridesharing or traditional bike sharing, act as a complement, rather than a substitute, for public transportation. Dockless shared bikes present a greener way of commuting, with significant environmental and societal impacts.

Assessing Strategies to Overcome Barriers for Drone Usage in Last-Mile Logistics: A Novel Hybrid Fuzzy MCDM Model

Effective last-mile (LM) delivery is critical to the efficient functioning of supply chains. In addition to speed and the cost of delivery, environmental and social sustainability are increasingly important factors in last-mile logistics (LML), especially in urban areas. Sustainable solutions such as drones attract special attention from researchers due to their high potential. The future of drone logistics is uncertain due to many barriers. This study analyzes, evaluates and ranks barriers to identify those that most significantly hinder broader drone adoption in LML, and proposes and ranks strategies to overcome them. This type of issue requires the involvement of multiple stakeholders with conflicting goals and interests. Therefore, the study employs a novel hybrid multi-criteria decision-making (MCDM) model that combines fuzzy Delphi-based fuzzy factor relationship (Fuzzy D-FARE) and fuzzy comprehensive distance-based ranking (Fuzzy COBRA) methods. The results indicate that the main obstacle to drone implementation in LM is the lack of aviation regulations. The risks of unauthorized access, data misuse, privacy breaches, and data security represent significant challenges. They are followed by ambiguously defined or burdensome requirements for insurance and liability for drone owners. The main contributions of this study are the establishment of a novel hybrid model, identification and ranking of barriers for broader application of drones in LML, and strategies for overcoming them.

Exploring the impacts of built environment on bike-sharing trips on weekends: The case of Guangzhou

Shared mobility has brought many disruptive changes to urban transportation systems all over the world. Shared bikes have proven to be among the most successful and influential travel tools in attempting to alleviate the last-mile problem – the difficulty in getting people from transportation stations to their final destinations. This study aims to investigate the impacts of built environment factors on bike-sharing trips. Although many studies have explored these impacts, most have focused on the impacts of urban function, and have paid insufficient attention to the cycling environment. This study used multi-source data, including street view images (SVIs), points of interest (POIs), digital elevation models (DEMs), and road networks, to fully identify the influences of the built environment from five dimensions. The multiscale geographically weighted regression (MGWR) method was used to investigate the impacts of the urban built environment on bike-sharing usage. The results found that high-density roads, recreational POIs, and residential POIs all had positive impacts on the volume of bike-sharing trips in residential areas on weekends, whereas urban greenness negatively impacts bike-sharing usage in parks, because of strict regulations promulgated by local governments. Moreover, the impacts of high-density street networks and residential communities had strong spatial non-stationarity, while the influences of other built environment factors, including road gradient, eye-level greenness, and urban function mixture, were demonstrated spatial stationarity. These findings can facilitate local governments’ and enterprises’ efforts to improve the cycling environment and ensure the efficient management of shared bikes.

Understanding bikeshare mode as feeder to bus:A case study of Chicago’s Divvy system

The bike-share system gives a decent solution to the first- and last-mile problem. And it connects trip origins/destinations and transit stations. However, current research in this area focuses more on metrobikeshare pattern, and there are still substantial gaps in research in the area of bus-bikeshare pattern. Focusing on Chicago, this study uses Divvy’s location information data to explore the spatial and temporal characteristics of bus-bikeshare transfer trips. Besides, we examine the built environment factors affecting bus-bikeshare transfers. This study uses buffers and time limits to isolate bus-bikeshare transfer trips. Two recognition rules proposed are a maximum transfer time of 20 min and a maximum buffer distance of 100 m. The findings are listed as follows: (1) the average distance and duration of bus-bikeshare trips is 1740.95m and 548.11s; (2) the bus-bikeshare trips show characteristics in different areas, seasons and periods; (3) there is a positive and significant spatial autocorrelation for the bus-bikeshare trips; (4) The bus-bikeshare pattern is influenced by built environment factors in a manner distinct from the bikeshare-only pattern, exhibiting its own unique characteristics.

Demand Prediction of Shared Bicycles Based on Graph Convolutional Network-Gated Recurrent Unit-Attention Mechanism

Shared bicycles provide a green, environmentally friendly, and healthy mode of transportation that effectively addresses the “final mile” problem in urban travel. However, the uneven distribution of bicycles and the imbalance of user demand can significantly impact user experience and bicycle usage efficiency, which makes it necessary to predict bicycle demand. In this paper, we propose a novel shared-bicycle demand prediction method based on station clustering. First, to address the challenge of capturing patterns in station-level bicycle demand, which exhibits significant fluctuations, we employ a clustering method that combines graph information from the bicycle transfer graph and potential energy. This method aggregates closely related stations into corresponding prediction regions. Second, we use the GCN-CRU-AM (Graph Convolutional Network-Gated Recurrent Unit-Attention Mechanism) model to predict bicycle demand in each region. This model extracts the spatial information and correlation between regions, integrates time feature data and local weather data, and assigns weights to the input features. Finally, experimental results based on the data from Citi Bike System in New York City demonstrate that the proposed model achieves a more accurate demand prediction.

Shared micromobility as a first- and last-mile transit solution? Spatiotemporal insights from a novel dataset

The first- and last-mile (FM/LM) problem is a major deterrent to public transit use. With the rise of shared micromobility options such as shared e-scooters in recent years, there is a growing interest in understanding their potential to serve as a last-mile transit solution. However, empirical data regarding the integrated use of shared micromobility and public transit have been limited so far. As a result, much is unknown regarding the spatiotemporal patterns and characteristics of shared micromobility trips serving as an FM/LM connection to transit. This paper addresses these knowledge gaps by leveraging a novel dataset (i.e., the Spin post-ride survey dataset) that records thousands of transit-connecting shared e-scooter trips in Washington DC. Specifically, we used the dataset to reveal the spatiotemporal patterns of transit-connecting shared e-scooter trips in Washington DC, resulting in some major policy insights regarding the integral use of shared e-scooters and public transit. We further leveraged the dataset to validate if and to what extent a commonly applied buffer-zone approach can infer FM/LM micromobility trips accurately. Statistical tests showed that the actual FM/LM Spin e-scooter trips differ from inferred FM/LM Spin e-scooter trips in both spatial and temporal dimensions. This indicates that the common practice of inferring FM/LM micromobility trips with a buffer-zone approach can lead to inaccurate estimates of transit-connecting micromobility trips.

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.

Navigating the final mile: how are Australian hospitals delivering autologous cell & gene therapies?

Navigating the final mile: how are Australian hospitals delivering autologous cell & gene therapies?

Navigating the final mile: how are hospitals providing capabilities for commercial cell therapy product delivery?

Navigating the final mile: how are hospitals providing capabilities for commercial cell therapy product delivery?

Rewards, Penalties and Real Case References: Improvement of the Last Mile Delivery in USA

The growth of the platform economy has fueled the rapid growth of the cargo transportation industry. However, while it continues to scale up, many problems of the existing freight transportation industry are being exposed - environmental damage, harming consumers, and even depriving producers of profits. The last-mile problem is particularly significant. The United States and China, two of the world's largest countries, have representative platform economy enterprises, namely Amazon and Taobao. These two companies' different freight models represent the two existing mainstream freight models in the world - free home delivery and stagecoach containers. By comparing the two companies' existing last-mile delivery problems and analyzing the two countries' different cultural backgrounds, this paper tries to solve the existing last-mile problems by introducing reward and punishment mechanisms and applying the two models to each other. In this way, enterprises in each country can learn from each other's distribution models and find the optimal solution for the platform economy distribution model.

Проєктний аналіз цифрових технологій в управлінні ланцюгом постачань

Most often, the possibilities of the applied digital technologies / tools are not used to their full extent, which is due to either the insufficient level of knowledge and competencies of the staff, or the complexity of the technical program implementation. At the same time, the methodology of digital transformation itself, especially in relation to logistics and supply chain management (SCM), is insufficiently developed, despite the existing works of foreign and domestic specialists in this field. The results of the analysis show the relevance and urgent need for the development of conceptual technological platforms of digital solutions for logistics services in supply chain management at the stage of using artificial intelligence and blockchain technology.
 A study was conducted on the possibility of using promising digital technologies in logistics and ULP, in particular Blockchain (distributed ledger systems), Cloud Services (cloud services), Augmented/Virtual Reality (AR/VR) (augmented / virtual reality), Big Data (big data analytics), Predictive Analytics, Industry 4.0 (Robots) and the Internet of Thing. The very procedure of digital transformation of supply chains should include a number of project solutions related to the formation of a communication network structure (Multi Party Net-work), in particular, using blockchain technology, an integrated supply chain planning system, as well as a digital platform for controlling and monitoring events in the chain supplies (Supply Chain Control Tower). Warehouse logistics is an industry that actively implements innovations. To work in the warehouse, special robots with the prefix AGV (literally – automatically controlled vehicle) are used. A project analysis is being conducted on the development of unmanned trucks using the existing developments of unmanned technologies of domestic car manufacturers and prototypes of unmanned cargo vehicles. The necessity of researching the prospects of robotization of warehouse operations and the use of unmanned vehicles (drones and trucks without drivers) in the distribution / delivery of Final Mile Logistics is shown.

Shared e-scooters as a last-mile transit solution? Travel behavior insights from Los Angeles and Washington D.C

Shared e-scooters are quickly emerging in the U.S., presenting potential to enhance public transit by serving as a last-mile feeder mode. However, much is unknown about user preferences and travel behavior regarding e-scooter and transit integration. This study addresses this knowledge gap by conducting a travel behavior survey in Washington D.C. and Los Angeles, California, both early adopters of dockless micromobility. We find that the last-mile problem deters transit use for about 70% of respondents, among whom over 60% have considered e-scooters for last-mile transit connections. However, this intention frequently did not translate into actual use due to a variety of barriers. We further developed ordinal and binary logit models to examine factors shaping intention and actual use of shared e-scooters as a last-mile solution. Results suggest the outcomes of interest are positively associated with transit use frequency and safety rating of e-scooter riding. Somewhat surprisingly, household income is not a significant variable in all models. Women and older adults are less likely to consider using e-scooters as a last-mile feeder mode. People of color and individuals without a college degree who ride e-scooters have a greater percentage of their shared e-scooter trips made to connect with transit compared to White people and those with a college degree. These findings contribute to the understanding of equity in shared micromobility. Finally, to promote transit and micromobility integration, policymakers may introduce bundled fares, integrated payment, promote e-scooter safety, increase e-scooters availability at transit stops, and improve bike lane infrastructure connecting to transit.

Deep self-learning enables fast, high-fidelity isotropic resolution restoration for volumetric fluorescence microscopy

One intrinsic yet critical issue that troubles the field of fluorescence microscopy ever since its introduction is the unmatched resolution in the lateral and axial directions (i.e., resolution anisotropy), which severely deteriorates the quality, reconstruction, and analysis of 3D volume images. By leveraging the natural anisotropy, we present a deep self-learning method termed Self-Net that significantly improves the resolution of axial images by using the lateral images from the same raw dataset as rational targets. By incorporating unsupervised learning for realistic anisotropic degradation and supervised learning for high-fidelity isotropic recovery, our method can effectively suppress the hallucination with substantially enhanced image quality compared to previously reported methods. In the experiments, we show that Self-Net can reconstruct high-fidelity isotropic 3D images from organelle to tissue levels via raw images from various microscopy platforms, e.g., wide-field, laser-scanning, or super-resolution microscopy. For the first time, Self-Net enables isotropic whole-brain imaging at a voxel resolution of 0.2 × 0.2 × 0.2 μm3, which addresses the last-mile problem of data quality in single-neuron morphology visualization and reconstruction with minimal effort and cost. Overall, Self-Net is a promising approach to overcoming the inherent resolution anisotropy for all classes of 3D fluorescence microscopy.

Location Optimization for Community Smart Parcel Lockers Based on Bilevel Programming

With the rapid development of e-commerce and the dramatic upsurge in direct-to-consumer deliveries, the last-mile problem has become increasingly apparent. With the distinct advantages of bringing economies of scale and providing 24/7 contactless self-service, smart parcel lockers play a critical role in solving the last-mile problem. However, due to a lack of planning, myopia expansion, and an ambiguous profit model, smart parcel locker suppliers in China have been suffering huge economic losses, restricting their further development. In the study, on the basis of an in-depth analysis of the cost elements and major revenue sources of smart parcel lockers, we propose a bilevel programming model to optimize the location of community smart parcel lockers with the upper-level model maximizing the profit of a third-party smart parcel locker supplier and the lower-level model maximizing user satisfaction. Then, a solution algorithm based on the genetic algorithm is proposed. Finally, some numerical experiments are carried out based on a medium-scale residential community in Jiading District, Shanghai. The sensitivity analyses conducted in this study reveal how the user satisfaction evaluation and the investment budget influence the expected profit. The modelling framework and numerical results can provide third-party smart parcel locker suppliers with significant theoretical support and practical guidance on planning the investment budget and optimizing the smart parcel locker locations to maximize their profit.

Impact of negotiation on subsidy scheme to incentivise the integrated use of metro and ride-hailing services

ABSTRACT Although the integration of metro and ride-hailing services is a promising way to handle the last-mile problem, the viability of long-term subsidies funded by municipalities with limited budgets is in doubt. We thus investigate two self-funded subsidy schemes: metro-lead subsidy scheme (MS) and ride-hailing platform-lead subsidy scheme (RS). To this end, an integrated system consisting of a metro operator and a ride-hailing platform is considered, where the participants have access to negotiation. A game-theoretic model is established to capture the interactions among self-interested stakeholders. The results across MS and RS show that bargaining behaviour is always effective in promoting traveller adoption of ‘metro + ride-hailing', but cannot leave both operators better off due to the adverse effect on the ride-hailing platform. Also, bargaining behaviour enables the preferences of the operators for MS and RS to be aligned, which are sensitive to metro coverage.