Impacts of spatial mismatch on commuting time of urban residents in China

In much of studies on spatial mismatch between residential and employer locations, job accessibility has been measured. However, the apparent disadvantages of the traditional measurement methods on the studies of Chinese cities have been noted.  This paper proposed an optimized method for job accessibility measurement by introducing the weigh coefficient of job opportunity, which quantifies the degree of uneven distribution of job opportunity in the Chinese cities. Take Nanjing city for example, this new method was used to measure the spatial distribution of job opportunity, investigate the spatial patterns and analyze the influences of job accessibility on commuting behavior. The results show that the distribution of job accessibility in Nanjing exhibits the different spatial patterns and mechanisms compared with US cases. <! [endif] --

The Effects of Campus Bump on Drivers’ Fixation Dispersion and Speed Reduction

To evaluate the effects of campus speed bumps on drivers’ speed and fixation distribution, a quasinaturalistic driving test was conducted on a Chinese campus. Seven randomly selected drivers, wearing the Dikablis eye tracking devices, were required to drive an OPEL SUV passing the speed bumps. The area close to the bump was divided into ten subsegments (15 m for each one). The degree of fixation dispersion within each subsegment was defined as the distance from each subcenter to the whole fixation center. All traffic data were recorded using mounted camera, and the trajectories were extracted in Matlab. The speed and trajectory data was divided into two groups: the before group for bump-free case and the after group for a 5 cm bump case. The observational before-after analysis shows statistical significance between the two cases. The individual vehicular speed analysis reveals that bump reduces nearly 60% of vehicles’ speeds to a certain extent within the distance from 30 m upstream to 15 m downstream. The drivers’ fixation points begin to disperse 30–45 m before they see the bump, and it falls back to normal level 15–30 m downstream of the bump. These findings will help engineers install speed bumps at the most appropriate locations

Dynamic Scheduling Model of Bike-Sharing considering Invalid Demand

System resources allocation optimization through dynamic scheduling is key to improving the service level of bike-sharing. This study innovatively introduces three types of invalid demand with negative effect including waiting, transfer, and abandoning, which consists of the total demand of bike-sharing system. Through exploring the dynamic relationship among users’ travel demands, the quantity and capacity of bikes at the rental points, the records of bicycles borrowed and returned, and the vehicle scheduling schemes, a demand forecasting model for bike-sharing is established. According to the predicted bikes and the maximum capacity limit at each rental point, an optimization model of scheduling scheme is proposed to reduce the invalid demand and the total scheduling time. A two-layer dynamic coupling model with iterative feedback is obtained by combining the demand prediction model and scheduling optimization model and is then solved by Nicked Pareto Genetic Algorithm (NPGA). The proposed model is applied to a case study and the optimal solution set and corresponding Pareto front are obtained. The invalid demand is greatly reduced from 1094 to 26 by an effective scheduling of 3 rounds and 96 minutes. Empirical results show that the proposed model is able to optimize the resource allocation of bike-sharing, significantly reduce the invalid demand caused by the absence of bikes at the rental point such as waiting in a place, walking to other rental points, and giving up for other travel modes, and effectively improve the system service level

Layout Optimization of Campus Bike-Sharing Parking Spots

The rapid development of bike sharing has posed some challenges to the traffic management on campus. The bike sharing on campus has problems such as messy parking, and some buildings in the peak hours have no bikes to borrow. Thus, alternative parking spots are proposed based on the layout principle of parking spots for bicycles. An optimization model of the layout of campus bike-sharing parking spots with travel time and construction cost as the optimization goal is established, and the branch and bound algorithm is used to solve the model. Finally, the study analysis is carried out by optimizing the layout of the bike-sharing parking spot of Nanjing University of Science and Technology. The results show that, after optimizing the layout of parking spots, the average travel time of users is reduced by 6.0%, and the total construction cost is reduced by 27.3%. While being convenient for campus bike-sharing users, it also provides scientific decision-making support for the campus traffic management

A Generalized Dynamic Potential Energy Model for Multiagent Path Planning

Path planning for the multiagent, which is generally based on the artificial potential energy field, reflects the decision-making process of pedestrian walking and has great importance on the field multiagent system. In this paper, after setting the spatial-temporal simulation environment with large cells and small time segments based on the disaggregation decision theory of the multiagent, we establish a generalized dynamic potential energy model (DPEM) for the multiagent through four steps: (1) construct the space energy field with the improved Dijkstra algorithm, and obtain the fitting functions to reflect the relationship between speed decline rate and space occupancy of the agent through empirical cross experiments. (2) Construct the delay potential energy field based on the judgement and psychological changes of the multiagent in the situations where the other pedestrians have occupied the bottleneck cell. (3) Construct the waiting potential energy field based on the characteristics of the multiagent, such as dissipation and enhancement. (4) Obtain the generalized dynamic potential energy field by superposing the space potential energy field, delay potential energy field, and waiting potential energy field all together. Moreover, a case study is conducted to verify the feasibility and effectiveness of the dynamic potential energy model. The results also indicate that each agent’s path planning decision such as forward, waiting, and detour in the multiagent system is related to their individual characters and environmental factors. Overall, this study could help improve the efficiency of pedestrian traffic, optimize the walking space, and improve the performance of pedestrians in the multiagent system

Demand, mobility, and constraints: Exploring travel behaviors and mode choices of older adults using a facility-based framework

The steady trend of aging has caused great concern on how cities should better accommodate the social needs of aged population. Older people in general have more leisure time than younger adults but are found highly constrained in daily travel. To examine the imbalance between travel demand and transport supply among older adults, this paper decomposes their daily travel into two categories (visits to non-ubiquitous and ubiquitous facilities) according to major characteristics of travel behaviors using Nanjing Household Travel Survey data. Multinominal logit (MNL) models are applied to exploit the effects of household and personal characteristics, trip characteristics, local supplies, and public transport services on travel mode choices. Results show that (i) travel demand and transport supply are highly unbalanced for most non-ubiquitous facilities, (ii) relative to younger adults, older adults travel further and highly rely on public transport to access non-ubiquitous facilities, (iii) providing more public transit services nearby non-ubiquitous facilities are more reliable to increase the accessibility of older adults than increasing the number of facilities. These results would help policy-makers better understand travel behaviors of older adults and develop strategies to accommodate their travel demand, especially from the perspective of facility network reorganization

Topological representations of crystalline compounds for the machine-learning prediction of materials properties

Abstract Accurate theoretical predictions of desired properties of materials play an important role in materials research and development. Machine learning (ML) can accelerate the materials design by building a model from input data. For complex datasets, such as those of crystalline compounds, a vital issue is how to construct low-dimensional representations for input crystal structures with chemical insights. In this work, we introduce an algebraic topology-based method, called atom-specific persistent homology (ASPH), as a unique representation of crystal structures. The ASPH can capture both pairwise and many-body interactions and reveal the topology-property relationship of a group of atoms at various scales. Combined with composition-based attributes, ASPH-based ML model provides a highly accurate prediction of the formation energy calculated by density functional theory (DFT). After training with more than 30,000 different structure types and compositions, our model achieves a mean absolute error of 61 meV/atom in cross-validation, which outperforms previous work such as Voronoi tessellations and Coulomb matrix method using the same ML algorithm and datasets. Our results indicate that the proposed topology-based method provides a powerful computational tool for predicting materials properties compared to previous works