A Simulation-Based Evaluation of a Cargo-Hitching Service for E-Commerce Using Mobility-on-Demand Vehicles

Transport accessibility and urban-rural connectivity are seen as critical aspects of rural economic development. In the transit network, passenger flow between urban-rural corridors demonstrates directional imbalances and low utilization of scarce resources. Freight transportation, on the other hand, lags due to poor geography, high operating costs, and scattered demand. This paper proposes a new mode of public transit that integrates passenger and freight transport, providing a carrier for logistics while compensating for the low utilization of passenger transport. In this mode, each timetabled round trip is divided into one dedicated passenger trip with high demand and one mixed-flow trip with on-demand requests. A space-time-state network is constructed considering the picking-up time window, loading/unloading service time, and electric bus energy replenishment. A mixed-integer linear programming model is developed to optimize the bus schedule that covers the travel demands and the charging requests with minimized travel costs. A Lagrangian relaxation framework with a dynamic programming algorithm and sub-gradient method is presented for problem-solving. The real-life rural-urban transport instance and a simulated network demonstrate the operation of the new mode and validate the efficiency of the proposed method. The innovative concept and the optimization framework are expected to serve as a reference for public administration to alleviate passenger and freight transportation bottlenecks in the urban-rural context.

This paper presents macro-level safety performance functions and aims to provide empirical tools for planners and engineers to conduct proactive analyses, promote more sustainable development patterns, and reduce road crashes. In the past decade, several studies have been conducted for crash modeling at a macro-level, yet in Italy, macro-level safety performance functions have neither been calibrated nor used, until now. Therefore, for Italy to be able to fully benefit from applying these models, it is necessary to calibrate the models to local conditions. Generalized linear modelling techniques were used to fit the models, and a negative binomial distribution error structure was assumed. The study used a sample of 15,254 crashes which occurred in the period of 2009–2011 in Naples, Italy. Four traffic analysis zones (TAZ) levels were used, as one of the aims of this paper is to check the extent to which these zoning levels help in addressing the issue. The models were developed by the stepwise forward procedure using explanatory Socio-Demographic (S-D), Transportation Demand Management (TDM), and Exposure variables. The most significant variables were: children and young people placed in re-education projects, population, population aged 65 and above, population aged 25 to 44, male population, total vehicle kilometers traveled, average congestion level, average speed, number of trips originating in the TAZ, number of trips ending in the TAZ, number of total trips and, number of bus stops served per hour. An important result of the study is that children and young people placed in re-education projects negatively affects the frequency of crashes, i.e., it has a positive safety effect. This demonstrates the effectiveness of education projects, especially on children from disadvantaged neighbourhoods.

Based on the analysis of the influencing factors of urban rail transit passenger flow transfer path selection behavior and the components of travel time, a method of estimating the proportion of urban rail transit passenger flow transfer path selection based on IC card data is proposed. Firstly, the research determines the shortest interchange path algorithm as Dijkstra algorithm and the graph-based depth-first search algorithm as the effective interchange path search algorithm; analyzes the factors influencing the passenger flow interchange path selection behavior as three types of travel time, interchange cost and road network familiarity; constructs the generalized cost function of effective interchange path selection by combining travel time and interchange cost; establishes the passenger flow probability selection model by combining road network familiarity. To eliminate the influence of absolute difference of utility on the probability of path selection in the polynomial logit model, the path probability selection model is improved with the help of minimum travel cost; Bayesian estimation is used to compare the passenger travel time with the theoretical travel time to identify the closest effective interchange path, and the joint distribution probability is used to calibrate the probability selection model in terms of travel time and interchange cost. The coefficient, coefficient of interchange passenger flow, penalty coefficient of interchange number, familiarity of road network and other related parameters are combined with the joint distribution probability to calibrate the probability of selection of different effective interchange paths.

The second line of the Beijing-Shanghai high-speed railway is planned to be completed in 2025. The opening of the second line of the Beijing-Shanghai high-speed railway will help ease the tight transportation capacity of the Beijing-Shanghai high-speed railway. This paper mainly studies the assignment of passenger flow to the Beijing-Shanghai high-speed railway and Beijing-Shanghai Second High-Speed Railway. The passenger flow is mainly generated in the cities along the Qingdao-Jinan Corridor and attracted to southern cities. Firstly, the volume of passengers in the cities along the Qingdao-Jinan Corridor is predicted according to the grey prediction method, and then the prediction results are applied to the gravity model to predict the OD passenger flow. After that, a high-speed railway network is established, and the k shortest path algorithm is used to find valid paths among each OD pair, taking travel time cost, fare and penalty cost into consideration of path impedance. The logit model is established to calculate the sharing rate of different paths, and the method of successive average is used to solve the passenger flow assignment model. Finally, this paper analyzes the impact of the fare rate level and travel time on the passenger flow assignment of the Beijing-Shanghai high-speed railway second line. The results show that when the fare rate of the Beijing-Shanghai high-speed railway second line is around 0.47, the passenger flow sharing on the high-speed rail network is basically stable.

Parking infrastructure is pervasive and occupies large swaths of land in cities. However, on-demand (OD) mobility has started reducing parking needs in urban areas around the world. This trend is expected to grow significantly with the advent of autonomous driving, which might render on-demand mobility predominant. Recent studies have started looking at expected parking reductions with on-demand mobility, but a systematic framework is still lacking. In this paper, we apply a data-driven methodology based on shareability networks to address what we call the “minimum parking” problem: what is the minimum parking infrastructure needed in a city for given on-demand mobility needs? While solving the problem, we also identify a critical tradeoff between two public policy goals: less parking means increased vehicle travel from deadheading between trips. By applying our methodology to the city of Singapore we discover that parking infrastructure reduction of up to 86% is possible, but at the expense of a 24% increase in traffic measured as vehicle kilometers travelled (VKT). However, a more modest 57% reduction in parking is achievable with only a 1.3% increase in VKT. We find that the tradeoff between parking and traffic obeys an inverse exponential law which is invariant with the size of the vehicle fleet. Finally, we analyze parking requirements due to passenger pick-ups and show that increasing convenience produces a substantial increase in parking for passenger pickup/dropoff. The above findings can inform policy-makers, mobility operators, and society at large on the tradeoffs required in the transition towards pervasive on-demand mobility.

Transport networks are operated under a pre-designed plan to carry out the trans- portation tasks. Perturbations can inevitably arise exogenously or endogenously, and cause disturbances to the transit of passengers or the delivery of freights. A transitional plan is needed to deal with the perturbations and accomplish the trans- port tasks. The transitional plan is referred to as perturbations management in this study. The first contribution of this work is that we construct the re-planning models for multi-class networks. The perturbation management for the single-class transport network, for instance, a passenger railway network is well studied, however, it is still missing for multi-class transport networks. In real applications, passenger flows and freight flow sometimes co-exist in one system. The volume of freight transport keeps increasing. On the other hand, there are usually multi-mode services associated to finish the transit of one object. The current research is centred around this point. We study perturbation management for multi-class networks, which involve the mixed passenger and freight railway networks and deploy various modes of service vehicles. There are several important research questions regarding the management of heterogeneous traffic flows in case of delays. The first challenge is to get an overview of the focus of the existing literature in rail bound networks and find out what are the promising directions for further research. Second, concerning the modeling part, the challenge is to come up with a replanning strategy and determine the control objective. Another task is to find out how to manage the different flows involved, either for the served clients or for the serving vehicles. Last concern is to explore how to achieve flexibility for the integrated network. The second contribution of this work is that the replanning models arrange the heterogeneous traffic according to their characteristics. When delays occur to a mixed passenger and freight railway network, the replanning gives more flexibility to the freight trains. The underlying reason is that the freight clients, i.e., the shippers care mainly about the finally delivery time instead of the taken route or the waiting time at the intermediate stations. While in the synchromodality replanning, the three modes of service are scheduled according to their applied sub-networks. In the multimodal transport network, the large capacity vehicles are aligned with the freight flows. Another contribution of this thesis is that the models achieve the synchronization of the involved flows. This is vital to perturbation management, even if the applied networks are different. We pursue the synchronization of flows from both time and space dimensions. We adjust the scheduling of the metro vehicles in agreement with the massive passenger flows (Chapter 3). We align the scheduling of three modes of service vehicles according to the delayed preceding vehicles or shipment release (Chapter 5). Meanwhile, the passenger transfer management (Chapter 3 and Chapter 4) and the freight flows re-assignment (Chapter 4 and Chapter 5) is integrated with the re-scheduling. The synergy of the rescheduling and re-assignment can optimally deploy the affected capacity to accomplish the transport tasks and minimize the operation costs.

Exploring parking choices under the coexistence of autonomous and conventional vehicles

The explosion of online shopping brings great challenges to traditional logistics industry, where the massive parcels and tight delivery deadline impose a large cost on the delivery process, in particular the last mile parcel delivery. On the other hand, modern cities never lack transportation resources such as the private car trips. Motivated by these observations, we propose a novel and effective last mile parcel delivery mechanism through car trip sharing, to leverage the available private car trips to incidentally deliver parcels during their original trips. To achieve this, the major challenges lie in how to accurately estimate the parcel delivery trip cost and assign proper tasks to suitable car trips to maximize the overall performance. To this end, we develop Car4Pac, an intelligent last mile parcel delivery system to address these challenges. Leveraging the real-world massive car trip trajectories, we first build up a 3D (time-dependent, driver-dependent and vehicle-dependent) landmark graph that accurately predicts the travel time and fuel consumption of each road segment. Our prediction method considers not only traffic conditions of different times, but also driving skills of different people and fuel efficiencies of different vehicles. We then develop a two-stage solution towards the parcel delivery task assignment, which is optimal for one-to-one assignment and yields high-quality results for many-to-one assignment. Our extensive real-world trace driven evaluations further demonstrate the superiority of our Car4Pac solution.

During daily subway operation, station closure has large impact on subway system organization and has received increasing attention. This article proposes an anomaly detection method based on ensemble algorithms to determine the range of station closure influence on passenger flow. Firstly, Ensemble Algorithm I is developed to identify the stations with passenger flow volume anomaly and origin-destination (OD) pairs with volume anomaly. Secondly, Ensemble Algorithm II is proposed to identify the OD pairs with travel time anomaly. Then, the spatial variation in passenger flow caused by station closure, i.e. shift of passenger flow to neighboring stations and shift of path flow, is analyzed, and the spatial-temporal influence range of station closure is determined. A case study of the Beijing subway system is performed to illustrate the validity of the proposed method.Compared with sub algorithm of ensemble learning and KNN algorithm, Ensemble Algorithm I and II are more robust and have less misjudgment.

In case of a metro disruption, the collaborative optimization for train rescheduling combined with passenger flow demand is quite essential since passenger flow is not adequately considered. To handle this gap, this study first establishes two separated optimization models, i.e. train rescheduling model and passenger flow model. On train side, short-turnings, fully cancellation or partial cancellation are taken into account to construct the disrupted timetable with the aims of minimizing train delays and timetable deviations from the original timetable. On passenger side, time-dependent OD passenger demand is incorporated into passenger’s arrival, passenger’s loading and alighting process. Then, an iterative framework is proposed to interact these two models. In each iteration loop, random short-turnings and cancellations are applied to calculate new departure and arrival for the services. Next, the corresponding departure time and arrival time is transferred to passenger flow model to evaluate the impacts on passenger’s travel time and the numbers of stranded passengers. The iterative results illustrates that less short-turning can effectively mitigate the numbers of stranded passengers at platforms but it comes at the cost of increasing train delays and passenger travel time. Cancelling trains can help to reduce passenger travel time as well but it should avoid causing heavier train delays to other non-cancelled services. Therefore, reasonable numbers of short-turnings and cancellations during disruption is trade-off between train delays and passenger delays during disruptions.

The subway average daily passengers in China has exceeded 80 million, with total energy consumption reaching 25 billion kWh in 2023, highlighting the critical importance of energy saving in subway stations. This study conducted on-site data collection at a subway station in Suzhou located in hot summer and cold winter climate zone in China in 2024, gathering data on outdoor meteorological parameters, indoor temperature and humidity, hourly passengers, and HVAC system energy consumption. EnergyPlus and the SARIMA are used to build the subway station energy simulation model and passenger flow model, respectively. The accuracy of these models is validated against measured data. The influencing factors of HVAC system energy consumption and their impacts are identified and quantified by correlation analysis. Results indicate that passenger flow exhibits a distinct dual-peak periodic pattern and has a highly significant impact on HVAC system energy consumption. Other influencing factors, in descending order of impact, include indoor air temperature, equipment power, humidity, ventilation volume, soil temperature, wind speed, and lighting power. The design capacity of the HVAC system is found approximately 30% larger than the actual demand according to real passenger flow. Optimizing the system design based on actual peak passenger data and dynamically operating the HVAC according to real-time passenger data can result in a 20% energy-saving rate. These findings provide valuable insights into the optimized design and energy-efficient operation of subway station HVAC systems.

The article considers the development of transport in the infrastructure system of Ukraine. Several main problems that hinder the development of transport infrastructure have been identified. The role of transport in the sphere of economic relations is determined. Transport, as an infrastructure sector, should provide timely and efficient freight and passenger transportation, promote the integration of Ukraine's economy into European and world economic systems. However, the current unsatisfactory condition of the rolling stock of various modes of transport in Ukraine causes a low level of use of Ukraine's potential as a transit country and, accordingly, reduces the competitiveness of the country's economy. Our state has significant potential in the development of air transport and increasing the volume of cargo and passenger traffic by air fleet of foreign and domestic production. But to realize this potential, it is important to adapt legislation to European standards. The importance of transport is that it provides links between industries, enterprises, regions of the country, foreign countries. It should be noted that the problems associated with the need to modernize the transport infrastructure of Ukraine do not lose their relevance over a long period of time. Transport is a necessary condition for the emergence and development of intensive exchange of goods between the individual territories involved in this division. The expansion of the territorial division of labor, its improvement and the emergence of new, more efficient forms largely depends on the level of development of transport. The role of the transport industry in the modern economy is very large. The efficiency of other industries and, consequently, the economic well-being of the country depend on its efficiency. The level of development of transport infrastructure, which provides the movement of freight and passenger flows, directly affects not only the socio-economic growth of territorial social systems. The developed transport system forms the "framework" of the regions and is the basis for the territorial division of labor. Transport is a necessary condition for the territorial division of labor, specialization of regions, and their integrated development.

Traditional public transportation mostly uses fixed schedules to meet society’s needs reliably. Alternatively, one can design a dynamic policy to update the schedule according to the state of the network. Using such policy, any dynamic changes in demand, travel times, traffic, geometry, or emergency modes will be reflected in the real-time scheduling. In this study, the application of reinforcement learning for public transportation scheduling and routing is proposed considering electric vehicles. Reinforcement learning is a right choice for online decision-making in a time-variant setting. Reflecting electric vehicles’ characteristics on public transportation scheduling supports the necessity of considering a dynamic scheduling policy, such as vehicle-to-grid transaction capability or dynamic charging strategies. The proposed scheduling methodology is evaluated for a customary six-stop network. The trained agent takes actions based on the number of waiting people, the drop-off requests, battery level, time, and the current location. The assumed models for the traffic load effect, the passenger flow per stops, and the electricity price add nonlinearity to the problem in which this algorithm will behave more conveniently compared to a fixed schedule setting. Using this method, the electric bus can choose the next destination, the optimal driving speed for each interval, and the amount of electricity to charge or to sell for optimal financial gain when connected to the charging infrastructure. The proposed environment is built numerically, and a learning-based agent is trained for this purpose. Our study supports that the optimal dynamic agent outperforms a fixed schedule policy in different modes of network performance, particularly, when the network undergoes abrupt demand changes.KeywordsReinforcement LearningRoutingPublic transitOptimizationSAC (soft actor-critic)Electric vehicle

The study considered the role of passenger road transport as one of the most important sectors of the national economy, which influences the entire economy of a country and its regions, as well as the quality of life of the population.The analysis of the transport system in the city of Khujand, which is mainly dominated by road transport, has been carried out based on the results of the conducted survey on passenger flows on bus routes of the city, particularly, regarding origin-destination matrix, fare collectability.The conclusions argue in favour of practicability of conducting regular systematic monitoring of passenger flows to optimize suggestions in the field of urban public transport development. In fundamental terms, main problems of passenger transportation management were identified including lack of a regular timetable on certain routes, non-compliance with the traffic schedule, resulting in an increase in travel time, changes in routing due to the absence of sufficient number of passengers, systematic violations of traffic rules, excessively long stops at the hub stopping points, etc. The suggestions comprise approaches intended to reduce influence of organisational and managerial factors on efficiency of passenger transportation, on the correct choice of rolling stock, that should be selected considering design features, possibility of serving all segments of the population (categories of passengers), traffic intensity, manoeuvrability, compliance with road conditions, etc. Conclusions were also made regarding techniques to justify the volume of passenger transportation, to identify average distance of passenger’s trip, to calculate technical, operational, and quality indicators of operation of public transport, distribution of passengers per routes.The analysis of the structure of passenger flows since it influences the indicators of the effectiveness of passenger transportation, plays the most significant role in the framework of development of new approaches to the solution of transport issues in the city of Khujand.Implementation of full-scale monitoring of passenger flows will make it possible to develop appropriate timetables facilitating movement of passenger flows on routes, to select a rational type of rolling stock in terms of capacity, will contribute to time-saving passenger travelling, socialisation of transport tariffs, and improved quality of transportation.

The urban rail transit network has gradually realized grid operation with the increase in the coverage rate. Therefore, the stopping schemes in accordance with the trend of the passenger flow are more conducive to improving the attractiveness of the rail transit and improving the sharing rate of the urban public transit. Traditional data from a single source may not be sufficient to describe the overall trend of the passenger flow in a period of time, and the error is possible in the case of insufficient data. Based on the multisource data, the spatial weight function is introduced to fuse the point of interest data and real estate information data, from which one obtains the residential index and office index, and the cluster analysis is conducted to obtain the potential stop scheme. Then, the optimization model of the train operation plan is established, aiming at minimizing the passenger travel time and the generalized system cost, and is constrained by a series of driving conditions. Compared with the single data source, multisource data can better reflect passenger flow trends and land use characteristics. Compared with the traditional all‐station stopping scheme, a reasonable setting of crossing stations and running express‐local trains can better satisfy the demands of the passenger flow. Finally, the optimization of Changchun rail Transit Line 1 shows that the model can reduce the travel time of passengers and the operating cost of the rail transit company and improve the quality of service, so as to achieve a win‐win situation between passengers and the rail transit company.