Demand Distribution Research Articles

A real-time NOx emission inventory from heavy-duty vehicles based on on-board diagnostics big data with acceptable quality in China

With the phase-in of China VI emission standard for heavy-duty vehicle (HDV), increasing numbers of HDVs are mandatorily equipped with on-board diagnostic (OBD) systems, providing opportunity to remote monitoring and supervision of emissions from in-use vehicles. However, the quality problem of OBD data is serious and lack of comprehensive assessment. In this study, the quality of nearly 1.6 billion OBD data from 38,692 China VI diesel HDVs travelling inside the Tangshan city of China was evaluated using a proposed framework which consists of validity indicators and signal transmission indicators. More than 24% of vehicles had overall invalidity rate over 20%, and only 0.4% of vehicles met the transmission period requirement of 1 Hz, indicating that the necessity and urgency for supervising and improving the OBD data quality. In particularly, several specific vehicle models were more prone to data quality problems, especially in the inability to determine the emission-related information. A real-time street-level NOx emission inventory was developed based on the OBD data with acceptable quality, using the direct monitoring and calculation of instantaneous emissions from each HDV. Compared to those from the top-down method which relied on gridded emissions and spatial allocation proxy schemes, the temporal and spatial characteristics of our results reflected the distribution and variations of actual freight demand in the real world. The developed real-world emission inventory will provide accurate support for the policy-making of precise control measures on HDVs.

Newsvendor conditional value-at-risk minimisation: A feature-based approach under adaptive data selection

The classical risk-neutral newsvendor problem is to decide the order quantity that maximises the expected profit. Some recent works have proposed an alternative model, in which the goal is to minimise the conditional value-at-risk (CVaR), a different but very much important risk measure in financial risk management. In this paper, we propose a feature-based non-parametric approach to Newsvendor CVaR minimisation under adaptive data selection (NPC). The NPC method is simple and general. It can handle minimisation with both linear and nonlinear profits, and requires no prior knowledge of the demand distribution. Our main contribution is two-fold. Firstly, NPC uses a feature-based approach. The estimated parameters of NPC can be easily applied to prescriptive analytic to provide additional operational insights. Secondly, unlike common non-parametric methods, our NPC method uses an adaptive data selection criterion and requires only a small proportion of data (only data from two tails), significantly reducing the computational effort. Results from both numerical and real-life experiments confirm that NPC is robust with regard to difficult and large data structures. Using fewer data points, the computed order quantities from NPC lead to equal or less downside loss in extreme cases than competing methods.

Optimization for vaccination demand allocation and distribution routes in pandemics based on a hierarchy decision model

Vaccination plays a significant role in interrupting the spread of pandemics, the process of moving the vaccine from the distribution center (DC) through the vaccination station and ultimately to the consumer involves many decisions. Which vaccination stations are open each day, how to allocate demand residents to the open stations, how many medical personnel to arrange at the open stations and the route of refrigerated vehicles to deliver vaccines to the open stations are issues that need to be addressed in this process. To improve the efficiency of vaccination and vaccine delivery during the actual vaccination scheduling process and to reduce the total cost, this study constructs two levels to describe the above problems and proposes a hierarchical decision mixed-integer linear program (MILP) that combines vaccine delivery and vaccination that jointly addresses the problems involved in both hierarchies. A case study of the COVID-19 vaccine administered at the Center for Disease Control and Prevention (CDC) in Hongshan District, Wuhan City, China, is then given to demonstrate the feasibility of the MILP and to highlight the managerial implications. It was found that opening more vaccination stations could reduce vaccination travel costs for the demand communities, opening a reasonable number of stations and rationally allocating the demand communities to these stations could reduce the costs of the whole system. This model also provides guidance for the CDC to address similar problems and has the potential to assist in the scheduling of public medical materials.

An end-to-end deep learning model for solving data-driven newsvendor problem with accessibility to textual review data

We investigate a data-driven single-period inventory management problem with uncertain demand, where large amounts of textual online reviews and historical data are accessible. Unlike two-step frameworks (i.e., predict-then-optimization), we propose an end-to-end (E2E) framework that directly suggests the order quantity by leveraging a deep learning model that inputs textual online reviews and other demand-related feature data, without any intermediate steps such as text sentiment analysis. The E2E model does not require any prior assumptions about the demand distribution and can automatically determine the order quantity that minimizes the newsvendor cost by employing the information from real-world data. Our experiments, using publicly available real-world data, demonstrate that our method can significantly reduce the sum of overage and underage costs, outperforming other data-driven models proposed in recent years. Specifically, the inclusion of textual online review data improves ordering decisions by a 28.7% cost reduction.

Using data to develop cost-effective battery-swap networks for electric vehicles

Electric vehicle (EV) development faces the challenges of range anxiety and the high construction and operating costs of charging and battery-swap infrastructure. To address these issues, this paper describes a proposed battery-swap network (BSN) comprising battery-swapping van service points and battery-swap stations to provide local, fast and cost-effective battery-swapping services. A data-driven approach was developed to analyse and model vehicle trajectory data to obtain realistic battery-swap demand distributions. An optimisation model based on location cover was then developed to minimise the cost of a BSN using a greedy algorithm. Real traffic network driving distance was adopted to improve authenticity and accuracy. In a case study, a BSN used a 1 week trajectory data set of taxis in Beijing, China and tested the effects in eight scenarios. The results showed that the mesh size of service points, service radius and coverage rate all had an impact on service quality and cost. It is concluded that a BSN could be cost-effectively deployed by adjusting these parameters to suit the needs of EVs at different development stages. This paper provides a useful reference for decision makers and operators to formulate policies and strategies.

Statistical modelling of dependence between net demands and deficits in two area power systems

Power system resource adequacy risks is dominated by the extremes of the relevant distributions or processes: the upper tail of demand and the lower tail of available generation. Because of this, relevant data in the historic record are sparse. Moreover, for interconnected systems, the degree of statistical dependence in these extremes between systems can also have a sizeable impact on the level of risk in each system. This paper uses results from statistical extreme value theory (EVT) to fit smoothed joint distributions for the values of (demand minus available renewables) and of surplus/deficit dependence in a two-area system, using data from the Irish and Great Britain power system for examples.As well as the statistical smoothing mitigating the consequences of limited volumes of data, the concept of asymptotic dependence provides a useful explanation of the strength of dependence. There is strong evidence that the deficits in the GB and Ireland systems are asymptotically independent, whereas there is evidence for asymptotic dependence in the distributions of (demand minus available wind capacity). This is consistent with the intuition that, when independent distributions of conventional capacity are convoluted with those of demand and wind, the dependence is weakened. The consequent use of a Gaussian copula to describe the dependence of deficits provides a convenient means of carrying out sensitivity analysis to the strength of relationship.

Inventory control strategy: based on demand forecast error

PurposeThis study examines the relationship between demand forecasting error and retail inventory management in an uncertain supplier yield context. Replenishment is segmented into off-season and peak-season, with the former characterized by longer lead times and higher supply uncertainty. In contrast, the latter incurs higher acquisition costs but ensures certain supply, with the retailer's purchase volume aligning with the acquired volume. Retailers can replenish in both phases, receiving goods before the sales season. This paper focuses on the impact of the retailer's demand forecasting bias on their sales period profits for both phases.Design/methodology/approachThis study adopts a data-driven research approach by drawing inspiration from real data provided by a cooperating enterprise to address research problems. Mathematical modeling is employed to solve the problems, and the resulting optimal strategies are tested and validated in real-world scenarios. Furthermore, the applicability of the optimal strategies is enhanced by incorporating numerical simulations under other general distributions.FindingsThe study's findings reveal that a greater disparity between predicted and actual demand distributions can significantly reduce the profits that a retailer-supplier system can earn, with the optimal purchase volume also being affected. Moreover, the paper shows that the mean of the forecasting error has a more substantial impact on system revenue than the variance of the forecasting error. Specifically, the larger the absolute difference between the predicted and actual means, the lower the system revenue. As a result, managers should focus on improving the quality of demand forecasting, especially the accuracy of mean forecasting, when making replenishment decisions.Practical implicationsThis study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.Originality/valueThis study established a two-stage inventory optimization model that simultaneously considers random yield and demand forecast quality, and provides explicit expressions for optimal strategies under two specific demand distributions. Furthermore, the authors focused on how forecast error affects the optimal inventory strategy and obtained interesting properties of the optimal solution. In particular, the property that the optimal procurement quantity no longer changes with increasing forecast error under certain conditions is noteworthy, and has not been previously noted by scholars. Therefore, the study fills a gap in the literature.

Setting fulfillment-time guarantees for accepting customer orders in a periodic-review base-stock inventory system

Retailers are increasingly promising a maximum fulfillment time window for fulfilling customer orders. Motivated by such guarantees, we consider an infinite-horizon periodic-review base-stock inventory system that imposes an endogenously determined fulfillment-time limit for accepted orders. The inventory position is reviewed every fixed T periods and an order is placed to bring the system inventory up to R, the target base stock. The replenishment lead time is a constant of L periods. In each period, the realized consumer demand is accepted only if it can be filled within a guaranteed time limit of τ periods. By incorporating a fulfillment-time limit, the model allows both backorders and lost sales. The objective is to find the strategy that minimizes the long-term average cost per unit time by selecting the base stock and guaranteed-delivery time limit. The behavior of the optimal R with respect to the guaranteed service time τ is investigated, and subsequently, the best value of τ is identified. Interestingly, when τ is greater than or equal to L, the best value of τ is independent of the holding cost and the base stock R. Regularity conditions are developed that assure the cost function is unimodal in R, guaranteeing the optimal base-stock solution can be found easily for a broad family of demand distributions. The solution procedure is extended to the case when the mean customer demand depends on the fulfillment time window promised by the retailer. We find greater customer sensitivity to the fulfillment time leads to a decrease in the optimal fulfillment time guarantee.

The effect of overconfidence in a robust competing game

PurposeThis study aims to investigate the effects and implications of overconfidence in a competitive game involving multiple newsvendors. This study explores how overconfidence influences system coordination, optimal stocking strategies and competition among newsvendors in the context of the well-known newsvendor stocking problem.Design/methodology/approachThe study applies robust optimization theory and the absolute regret minimization criterion to analyze the competitive game of overconfident newsvendors. This study considers the asymmetric information held by newsvendors regarding market demand and obtains a closed-form solution for the competing game. The effects of overconfidence on system coordination and optimal stocking strategies are examined.FindingsThe results of the study indicate that overconfidence can act as a positive force in reducing the effects of overstocking caused by competition and asymmetric information among newsvendors. The analysis reveals that there exists an optimal level of overconfidence that coordinates the ordering system of multiple overconfident newsvendors, leading to first-best outcomes under certain conditions. Additionally, numerical examples confirm the obtained results. Furthermore, considering newsvendors' expected profit, the study finds that a higher degree of overconfidence does not necessarily result in lower actual expected profit.Research limitations/implicationsDespite the significant contributions of this study to theoretical and managerial insights, this study does have certain limitations. First, in the establishment of the belief demand function, the substitution ratio, which quantifies the transfer, is assumed to be an exogenous variable. However, in reality, this is often influenced by factors such as the price of goods and the distance between stores. Therefore, one direction worth studying in the future is to explore the uncertainty associated with the demand substitution ratio and integrate that as an endogenous variable into the optimization model. Second, this study does not address the type of product and solely focuses on quantitatively analyzing the effect of salvage value on the optimal stocking strategy. Future studies can explore the effect of degree of perishability and selling period of the product on the stocking. Third, the focus of uncertainty in this study revolves around market demand, and the implications of this uncertainty are significant. A recent study (Rahbari et al., 2023) addressed an innovative robust optimization problem related to canned foods during pandemic crises. The recent study's findings highlighted the effectiveness of expanding canned food exports to neighboring countries with economic justification as the best strategy for companies amidst the disruptions caused by the coronavirus disease 2019 (COVID-19) pandemic. Incorporating the issue of disruptions into the authors' research would be interesting and challenging.Practical implicationsFrom a managerial perspective, the authors' study provides a research paradigm for game-theoretic inventory problems in scenarios where the market demand distribution is unknown. While most inventory problems are analyzed and solved based on expectation-based optimization criteria, which rely on an accurate distribution of market demand, obtaining this information in practice can often be challenging or expensive for decision-makers. Consequently, a discrepancy arises between real-world observations and theoretical identifications. This study aimed to complement previous research and address the inconsistency between observations and theoretical identification.Social implicationsThe authors' research contributes to the existing understanding of overconfidence and assists individuals in making appropriate stocking strategies based on the individuals' level of overconfidence. Diverging significantly from the traditional view of overconfidence as a negative bias, the authors' results show the view's potential positive impact within a competitive environment, resulting in greater actual expected profits for newsvendors.Originality/valueThis study contributes to the existing literature by examining the effects of overconfidence in a competitive game of newsvendors. This study extends the analysis of the well-known newsvendor stocking problem by incorporating overconfidence and considering the implications for system coordination and competition. The application of robust optimization theory and the absolute regret minimization criterion provides a novel approach to studying overconfidence in this context.

Inventory Control and Learning for One-Warehouse Multistore System with Censored Demand

Efficient Learning Algorithms for Dynamic Inventory Allocation in Multiwarehouse Multistore Systems with Censored Demand Motivated by collaboration with a prominent fast-fashion retailer in Europe, the researchers focus their attention on the one-warehouse multistore (OWMS) inventory control problem, specifically addressing scenarios in which the demand distribution is unknown a priori. The OWMS problem revolves around a central warehouse that receives initial replenishments and subsequently distributes inventory to multiple stores within a finite time horizon. The objective lies in minimizing the total expected cost. To overcome the hurdles posed by the unknown demand distribution, the researchers propose a primal-dual algorithm that continuously learns from demand observations and dynamically adjusts inventory control decisions in real time. Thorough theoretical analysis and empirical evaluations highlight the promising performance of this approach, offering valuable insights for efficient inventory allocation within the ever-evolving retail industry.

Urban Resident Travel Survey Method Based on Cellular Signaling Data

A low-cost, timely, and durable long-term approach to resident travel surveys is crucial for authorities to understand the city’s transportation systems and formulate transportation planning and management policies. This paper summarizes commonly used wireless positioning technologies and uses the STDBSCAN method to identify travel endpoints based on the characteristics of trajectory location information. It uses Shenzhen cellular signaling data to visually analyze the spatial and temporal distribution of urban traffic demand, traffic correlation, and asymmetry of traffic flow between different traffic zones. The results confirm that mobile internet information represented by cellular signaling information can effectively reflect the traffic status of urban areas, which, compared to traditional travel survey methods, has the advantages of lower cost, more timely feedback, and can be durably carried out in the long term.

Coupled MOP and PLUS-SA Model Research on Land Use Scenario Simulations in Zhengzhou Metropolitan Area, Central China

Land use simulations are critical in predicting the impact of land use change (LUC) on the Earth. Various assumptions and policies influence land use structure and are a key factor in decisions made by policymakers. Meanwhile, the spatial autocorrelation effect between land use types has rarely been considered in existing land use spatial simulation models, and the simulation accuracy needs to be further improved. Thus, in this study, the driving mechanisms of LUC are analyzed. The quantity demand and spatial distribution of land use are predicted under natural development (ND), economic development (ED), ecological protection (EP), and sustainability development (SD) scenarios in Zhengzhou based on the coupled Multi-Objective Programming (MOP) model and the Patch-generating Land Use Simulation model (PLUS) considering Spatial Autocorrelation (PLUS-SA). We conclude the following. (1) The land use type in Zhengzhou was mainly cultivated land, and 83.85% of the land for urban expansion was cultivated land from 2000 to 2020. The reduction in forest from 2010 to 2020 was less than that from 2000 to 2010 due to the implementation of the policy in which farmland is transformed back into forests. (2) The accuracy of PLUS-SA was better than that of the traditional PLUS and Future Land Use Simulation (FLUS) models, and its Kappa coefficient, overall accuracy, and FOM were 0.91, 0.95, and 0.29, respectively. (3) Natural factors (temperature, precipitation, and DEM) contributed significantly to the expansion of cultivated land, and the increase in forest, grass, and construction land was greatly affected by socioeconomic factors (population, GDP, and proximity to town). (4) The land use structure will be more in line with the current requirements for sustainable urban development in the SD scenario, and the economic and ecological benefits will increase by 0.75 × 104 billion CNY and 1.71 billion CNY, respectively, in 2035 compared with those in 2020. The PLUS-SA model we proposed had higher simulation accuracy in Zhengzhou Compared with the traditional PLUS and FLUS models, and our research framework can provide a basis for decision-makers to formulate sustainable land use development policies to achieve high-quality and sustainable urban development.

Adaptation to seasonal drought in irrigation districts of south China: A copula-based fuzzy-flexible stochastic multi-objective approach for precise irrigation planning

The occurrence of seasonal drought has led to periodic water shortages in irrigation districts of south China, posing significant agricultural challenges on water and food security. Consequently, there is a pressing need for high-efficient water resources management from a sustainability perspective. To address this issue, a copula-based fuzzy-flexible stochastic multi-objective programming (CFSMP) model was proposed to facilitate irrigation planning under the combined influence of seasonal dry and wet conditions. The model was aimed to simultaneously achieve maximum economic benefit, water productivity and green water use efficiency, considering the economic, social, and environmental spheres of the irrigation district. The CFSMP model represented an innovative solution that was capable of (1) precisely describing the response of crop yield to water supply at ten-day time scale, which can be matched with practical irrigation demand; (2) characterizing joint distributions of seasonal dry and wet conditions of precipitation during different crop growth periods based on the copula function; (3) reconciling conflicts on objectives among economic, social, and environmental spheres of the irrigation district in complex and uncertain environments. The proposed model was applied to the Dongfeng Reservoir Irrigation District in south China. The downscaled ten-day Jensen water production functions of main grain and cash crops were fitted to recognize precise distributions of crop water demand and water sensitivity index. The Frank copula, with the smallest value of squared Euclidean distribution, was selected to describe the joint distribution of seasonal precipitation and gain insights into actual seasonal water availability and water scarcity. The results showed that the proposed model produced more water-saving and yield-promoting irrigation planning policies than the current quota. The average water allocation of rice, wheat, maize, rape, and citrus decreased by 352.6 mm, 10.6 mm, 67.7 mm, 7.44 mm, and 54.6 mm, respectively. Except for a yield reduction of 8.8% for rape, crop yields of rice, wheat, maize, and citrus were improved by 33.3%, 45.4%, 29.9%, and 12.3%, respectively. The coordination degree of the model reached 0.827, demonstrating excellent performance on generating coordinated and robust solutions. Therefore, the CSFMP model has the potential to alleviate seasonal drought in south China and can be applied in similar regions with comparable resources crisis.

A heuristic approach for multi-echelon inventory optimisation in a closed-loop supply chain

This study deals with a closed-loop supply chain where inventory levels are controlled by an order-up-to inventory policy. The system under consideration is the cylinder-packaged gas supply chain of Air Liquide company, where empty cylinders used by customers are returned to be filled again by company plants. First, we examine the goodness of fit for demand distributions based on company real data. This enables us to better characterise demands pertaining to different classes of products. Then, we formulate the multi-echelon serial inventory model to be optimised and propose a heuristic to compute the target inventory levels that helps in achieving the desired customer service level while minimising the total inventory cost. The proposed heuristic is easy to implement in the field and gives results close those obtained using a simulation-optimisation approach that is more time-consuming. Finally, we perform a numerical analysis based on company real data and compare several methods that can be used to compute the target inventory levels by varying mainly two assumptions: parameters regarding demand distributions and metrics used to assess customer service levels.

A method for water supply network DMA partitioning planning based on improved spectral clustering

Abstract Recently, the scale and complexity of water distribution networks (WDNs) have been increasing with the acceleration of urbanization process. It has become a hot research focus to use district metering area (DMA) for more efficient management and control of WDNs. This article proposes a multistage DMA planning method based on improved weighted spectral clustering and genetic algorithm, aiming to address issues such as high investment cost and large differences in the water network demand distribution. First, the actual case pipe network is transformed into an undirected weighted graph based on graph theory, and a similarity matrix is formed by combining the physical properties and hydraulic characteristics of the water network. Then, based on the similarity matrix, the weighted spectral clustering algorithm is used to preliminarily divide the WDN, and the performance of the water supply pipe network formed with different division quantities and different weighting schemes is discussed. Finally, the genetic algorithm is used to optimize the arrangement of valves and flow meters on boundary pipes to generate the final configuration of DMA. The results show that the proposed method has a significant improvement in pipe network topology, hydraulic performance index, and economy compared with the traditional DMA method.

A heuristic approach to the stochastic capacitated single allocation hub location problem with Bernoulli demands

Hubs are critical components of transportation and distribution systems, and hub networks play a special role in freight and passenger transportation services worldwide. This paper studies a capacitated single allocation hub location problem with Bernoulli demands. Since the origin-destination (OD) demands are stochastic in nature, and the nodes are allocated to hubs before knowing their realized values, the actual total demand allocated to each hub is uncertain. Therefore, demand can exceed the capacity of hubs, rendering a need for outsourcing. The problem is studied under two distinct outsourcing policies, namely the facility and customer outsourcing. Mathematical models are developed for each case as two-stage stochastic programs. Deterministic equivalent formulations are obtained for problems, assuming a homogeneous demand distribution for all OD pairs. A Tabu Search-based algorithm is presented as a solution approach to deal with large problem instances. Extensive computational tests demonstrate the outstanding performance of the developed models and the metaheuristic procedure in terms of solution quality and computational time. The relevance of using stochastic programming approach for the problems is demonstrated via computational results. This study also reports solutions for problems with 200 nodes for the first time.

Optimization of headway and bus stop spacing for low demand bus routes

ABSTRACT We propose a methodology for optimization of service headway and stop spacing along a low-demand bus route that minimizes operator and user costs. This study develops analytical cost models that are representative of low-demand routes by using negative binomial distribution for passenger demand for boarding and alighting pattern to estimate the probability of stopping and both random and planned arrival of passengers are considered to estimate the waiting time. Pareto optimal solutions obtained using multi-objective evolutionary algorithm, NSGA-II indicate that optimal values of headway and stop spacing are underestimated if optimized based on assumptions typical of high-demand routes which is passenger demand for boarding and alighting at bus stops randomly following a Poisson process. With the aid of the study methodology, transit planners will be able to improve the service utilization and passenger accessibility along an under-performing low demand routes by recommending minimal modifications to the existing route and bus schedule.

Myocardial perfusion and flow reserve in the asynchronous heart: mechanistic insight from a computational model.

The tight coupling between myocardial oxygen demand and supply has been recognized for decades, but it remains controversial whether this coupling persists under asynchronous activation, such as during left bundle branch block (LBBB). Furthermore, it is unclear whether the amount of local cardiac wall growth, following longer lasting asynchronous activation, can explain differences in myocardial perfusion distribution between subjects. For better understanding of these matters, we built upon our existing modeling framework for cardiac mechanics-to-perfusion coupling by incorporating coronary autoregulation. Regional coronary flow was regulated with a vasodilator signal based on regional demand, as estimated from regional fiber stress-strain area. Volume of left ventricular wall segments was adapted with chronic asynchronous activation towards a homogeneous distribution of myocardial oxygen demand per tissue weight. Modeling results show that: (1) both myocardial oxygen demand and supply are decreased in early-activated regions and increased in late-activated regions; (2) but that regional hyperemic flow remains unaffected; while (3) regional myocardial flow reserve (the ratio of hyperemic to resting myocardial flow) decreases with increases in absolute regional myocardial oxygen demand as well as with decreases in wall thickness. These findings suggest that septal hypoperfusion in LBBB represents an autoregulatory response to reduced myocardial oxygen demand. Furthermore, oxygen demand-driven remodeling of wall mass can explain asymmetric hypertrophy and the related homogenization of myocardial perfusion and flow reserve. Finally, the inconsistent observations of myocardial perfusion distribution can primarily be explained by the degree of dyssynchrony, the degree of asymmetric hypertrophy, as well as the imaging modality used.

Where to park an autonomous vehicle? Results of a stated choice experiment

The future innovation and growing popularity of autonomous vehicles have the potential to significantly impact the spatiotemporal distribution of parking demand. However, little knowledge is gained on how people will choose to park their autonomous cars. In principle, an autonomous vehicle is not necessarily parked close by like traditional vehicles leveraging the automated driving and parking capability, still, the decision made by people is important for policymakers in urban and transportation planning. This study attempts to gain useful insights to understand people’s parking location choices for autonomous vehicles. A stated choice experiment was designed, allowing people to choose a parking location for autonomous vehicles in varied contexts, including time windows, picking-up times, and the requirement for on-time arrival at the next activity. We found that similar to conventional cars people generally prefer cheaper and/or closer parking lots for autonomous vehicles. However, the distance between a parking lot and the activity location is relatively longer in the case of autonomous vehicles. The amount of time an autonomous vehicle spends in congestion while picking up the users influences the choice of parking locations. Moreover, substantial preference heterogeneity between individual people was found in the parking choice behavior. The maximum value of access time for autonomous cars is 34 $/h which is higher than the empirical value of walking time for conventional cars. Results of elasticity indicate that the influence of parking fees is larger than that of access time and congestion time.

Understanding dockless bike-sharing spatiotemporal travel patterns: Evidence from ten cities in China

Dockless bike-sharing (DBS) has gained popularity in many cities in recent years. However, little attention has been given to the commonalities and differences in DBS travel patterns from multiple cities. This study aims to fill this gap by comparing DBS travel patterns in ten Chinese cities of varying scales. It collected data from these cities and analysed them from two perspectives: service usage characteristics and complex network properties. The results reveal a coexistence of similarities and differences in DBS travel patterns across different cities. The ten major cities show significant similarities in temporal distribution, with a similarity coefficient exceeding 0.7. Southern cities have a higher percentage of night riding compared to their northern counterparts. Moreover, over 85% of the travel distances are within 2 km. Concerning spatial distribution, all ten cities demonstrate an imbalanced demand distribution during peak hours, particularly in downtown areas, exhibiting evident tidal patterns. Network analysis outcomes demonstrate that the DBS networks in all ten cities possess small-world and scale-free properties. Furthermore, nodes with high degree centrality and closeness centrality are predominantly concentrated in downtown areas, displaying a diminishing trend from the city centre to the outskirts and showcasing strong global spatial autocorrelation. The betweenness centrality exhibits a property of random distribution. Nodes with high PageRank values are mainly concentrated in the city centre and around metro stations. The findings offer valuable insights for transportation planners and managers seeking to enhance their understanding of DBS systems.