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Bus bunching is of particular concern and undesirable for both operators and passengers in high-frequency transit. In contrast to existing control methods, this paper proposes a novel control policy, namely, providing real-time bus crowding information (BCI) for passengers. It is believed that passengers would spontaneously distribute more evenly among buses and help to prevent bus bunching with the provision of BCI accompanying arrival time information due to the following mechanism. A proportion of passengers would be likely to wait a few more minutes for the next bus when the current bus is crowded and the next bus is more comfortable, and the boarding times of these passengers would make the next bus dwell longer and increase its headway from the previous bus. We formulate bus motion models incorporating passenger boarding choice under BCI to realize the policy in simulation experiments. The results demonstrate that the policy can reduce operation instability by approximately 20% in terms of bus headway and single-trip time. In addition, this policy can significantly reduce the in-vehicle crowdedness experienced by passengers by up to 25% at the cost of small increases in the mean journey time in some cases. The simulation experiments on a holding-controlled route also indicate that the proposed policy is able to coordinate with holding well. A sensitivity analysis further confirms that the policy’s performance is robust even if the passengers have low inclinations to choose the next bus. The policy of providing BCI in this paper is especially effective for bus routes with high passenger demand and may have great application potential in practice.

China has been witnessing prominent demographic ageing because of its sustained low fertility (one-child policy) and mortality rates. In 2017, nearly one in four elderly adults in the world live in China. The rapid increase of the elderly population is supposed to dramatically influence the urban and transportation system. Active travel plays an important role for the ageing Chinese population to sustain their mobility and wellbeing. To provide suitable policy implications for age-friendly travel environments in China, this study investigates how the built environment affects active travel behavior. Particularly, we explore the influences of built environment on daily active travel frequency and time expenditure while taking into account travel attitudes. A zero-inflated ordered probit model and a Cox proportional hazards model are respectively estimated based on the Nanjing Travel Survey data. Results show that the social and cultural contexts exert pronounced impacts on the travel pattern of Chinese older people. Specifically, it is found that the living pattern of co-residence, and the proximity to market, park/square, and chess/card room are influential in shaping active travel patterns. In addition, the built environment shows larger effects on the active travel behavior of older adults than on that of young people. Attitudes towards active travel are not significant in explaining the senior's travel behavior, indicating limited self-selection effects. The findings will offer insights to establish effective and appropriate land use strategies and public facility distribution for the elderly during the Chinese urban renewal process.

The analysis of travel mode choice is important in transportation planning and policy-making in order to understand and forecast travel demands. Research in the field of machine learning has been exploring the use of random forest as a framework within which many traffic and transport problems can be investigated. The random forest (RF) is a powerful method for constructing an ensemble of random decision trees. It de-correlates the decision trees in the ensemble via randomization that leads to an improvement of forecasting and reduces the variance when averaged over the trees. However, the usefulness of RF for travel mode choice behavior remains largely unexplored. This paper proposes a robust random forest method to analyze travel mode choices for examining the prediction capability and model interpretability. Using the travel diary data from Nanjing, China in 2013, enriched with variables on the built environment, the effects of different model parameters on the prediction performance are investigated. The comparison results show that the random forest method performs significantly better in travel mode choice prediction for higher accuracy and less computation cost. In addition, the proposed method estimates the relative importance of explanatory variables and how they relate to mode choices. This is fundamental for a better understanding and effective modeling of people’s travel behavior.

Dockless bike sharing (DBS), as an emerging bike sharing scheme, effectively promotes active travel and improves the mobility of users. Travel behavior analysis of DBS users is a key basis of service improvement, and the data sources include user surveys and trip records. User surveys are widely applied but have the drawback of perceptual errors. Thus, there is a compelling need for exploring travel behavior using automatically-collected trip records. This study examines DBS users' travel frequency and purpose with five semi-annual user surveys and trip record data in Nanjing, China. User surveys reflect user perception of travel behavior, while trip records can be used to calculate actual travel frequency and infer travel purpose. The results show that perceptual travel frequency based on user surveys has a central tendency, and actual travel frequency based on trip records follows a heavy-tailed distribution. Older people tend to underestimate their travel frequency, but other age groups tend to overestimate their frequency. In addition, a dockless bike sharing's travel purpose inferring (DBS-TPI) model is proposed to infer the travel purpose based on trip record data and points of interest data. The DBS-TPI model is reliable and transferable under different scenarios, which could complement user surveys. This study provides insights into improving data collection for better understanding of travel behaviors among DBS users.

This paper proposes a continuum approximation (CA) modeling framework to optimize the hybrid transit network designed with grids in the central district and hub-and-spoke structure in the periphery. Two CA models are formulated incorporating the local route service and the short-turn strategy respectively. The transit network configuration is optimized through minimizing the objective functions, which consider costs pertinent to passengers and operating agency. The decision variables include service boundary, spacings and headways of the regular service and the complementary services. Numerical experiments show that the performances of CA models with different complementary services are quite distinct under various demand scenarios.

Smart card (SC) data has become one of the major data sources for transit passengers’ behavior analysis, network modeling, and control optimization. Origin–destination (O–D) estimation has been recognized as a requisite step before utilizing the smart card data to investigate transit passengers’ spatiotemporal dynamics or conduct other SC data-based transit modeling. In the recent decade, the extant literature has proposed various trip-chain-based methods for transit O-D estimation using SC data. However, one problem of the conventional trip-chaining estimation approach has been noticed but not paid enough attention to: O-D estimation of single transactions cannot be conducted since the trip-chain method generally requires at least two trip records per day to proceed with. Such a flaw in the classic trip-chain approach might lead to a considerable amount of data loss and inaccurate O-D estimation. This paper improved the existing trip-chain O-D estimation method by introducing a new framework based on the Minimum Entropy Rate (MER) criterion. The proposed MER-based method adopts a similar mechanism of noise reduction in information theory. The basic idea of our approach is to infer the alighting location of single trips using alternative stops that preserve passengers’ travel regularity exhibiting in their mobility sequences. Our enhanced approach can estimate alighting stops for single trips with decent accuracy, thus preventing a potential massive data loss. Moreover, the study also provides an in-depth insight into the relationship between entropy rates estimated using trip sequences and passengers’ travel regularity. The estimation results can further benefit future transit studies with reliable data sources.

This paper reports that the sodium–potassium (Na–K) lidar was completed in November 2016 at Sao Jose Dos Compose, Brazil (23°S, 45°W), by the joint effort of the National Space Science Center, Chinese Academy of Sciences (NSSC) and Instituto Nacional de Pesquisas Espaciais (INPE). This system realized the Na and K metal layers simultaneously observe in Brazil, and this is the first instance of K layer detection in South America. Some of the key parameters and technologies have been optimized based on the Na and K layer dual-wave lidar in Beijing Yanqing station, such as improve technical parameters for receiving telescope, the narrow linewidth, efficient laser frequency doubling, the wavelength automatic locking techniques. By adopting these technologies, the output were 589 nm and 770 nm lasers, with high emission powers of 75 mJ and 83 mJ, respectively, and backscattered signals of Na and K layers with high signal quality were obtained. Observation data showed that the original echo photon count of the Na layer was approximately 42,486 (time resolution: 200 s, spatial resolution: 96 m) and the number of noise photons was 286 in a single data acquisition. The signal-to-noise ratio was up to 205:1. At the same spatiotemporal resolution, the original echo photon count of the K layer was approximately 1633, the noise photons were 38, and the signal-to-noise ratio was up to 40:1. The initial photocounts received has demonstrated that the Brazil K lidar has produced high quality signal with signal-to-noise level required by intended science studies. Moreover, the simultaneous phenomena of sporadic Na (Nas) and sporadic K (Ks), and the highly concentrated layers of atomic K have been observed, the K density in these narrow layers exceeds 1019 cm−3.

The station-based bike sharing system (SBBSS) and the free-floating bike sharing system (FFBSS) have been adopted on a large scale in China. However, the overlap between the services provided by these two systems often makes bike sharing inefficient. By comparing the factors that affect the usage of the two systems, this paper aims to propose appropriate strategies to promote their coordinated development. Using data collected in Nanjing, a predictive model is built to determine which system is more suitable at a given location. The influences of infrastructure, demand distribution, and land use attributes at the station level are examined via the support vector machine (SVM) approach. Our results show that the SBBSS tends to be favored in areas where there is a high concentration of travel demand, and close proximity to metro stations and commercial properties, whereas locations with a higher density of major roads and residential properties are associated with more frequent use of the FFBSS. With regard to the methods used, a comparison of several machine learning approaches shows that the SVM has the best predictive performance. Our findings could be used to help policy makers and transportation planners to optimize the deployment and redistribution of docked and dockless bikes.

In this paper, we proposed a new method to extract travel patterns for transit riders from different public transportation systems based on temporal motif, which is an emerging notion in network science literature. We then developed a scalable algorithm to recognize temporal motifs from daily trip sub-sequences extracted from two smart card datasets. Our method shows its benefits in uncovering the potential correlation between varying topologies of trip combinations and specific activity chains. Commuting, different types of transfer, and other travel behaviors have been identified. Besides, varying travel-activity chains like “Home → Work → Post-work activity (for dining or shopping) → Back home” and the corresponding travel motifs have been distinguished by incorporating the land use information in the GIS data. The analysis results contribute to our understanding of transit riders’ travel behavior. We also present application examples of the travel motif to demonstrate the practicality of the proposed approach. Our methodology can be adapted to travel pattern analysis using different data sources and lay the foundation for other travel-pattern related studies.

This paper addresses a static bike repositioning problem by embedding a short-term demand forecasting process, the Random Forest (RF) model, to account for the demand dynamics in the daytime. To tackle the heterogeneous repositioning fleets, a novel repositioning operation strategy constructed on the hub-and-spoke network framework is proposed. The repositioning optimization model is formulated using mixed-integer programming. An artificial bee colony algorithm, integrated with a commercial solver, is applied to address computational complexity. Experimental results show that the RF can achieve a high forecasting accuracy, and the proposed repositioning strategy can efficiently decrease the users’ dissatisfaction.

In recent years, free-floating bike sharing (FFBS) has been rapidly promoted in China, attracting numerous users and consuming many resources. The extensive supply and inefficient repositioning of FFBS are challenges for sustainable development, and put forward higher requirements for parking planning. This paper estimates the parking demand of all three FFBS companies by combining the journey data of Mobike in Nanjing and the Nanjing FFBS bike survey. Three clustering methods were applied to determine the virtual stations of bike aggregation. The maximum number of bikes in a day is recognized as the parking demand in each virtual station. The results show that more than half of bikes are in low turnover rate, and the management of FFBS should be improved. The K-means method turns out to observe the best clustering result for determining virtual stations. The demand for parking spaces of all FFBS companies in Nanjing is estimated accordingly. In addition, the relations of bicycle supply, repositioning and parking are discussed, showing the impact of parking demand on greenhouse gas (GHG) emissions. The research could help to propose an appropriate plan for meeting FFBS parking demand, and have enlightening sight of emissions reduction and sustainable development in the FFBS services.

Research on travel behavior of the urban low-income citizens in China is minimal. The main objective of this study is to examine the travel characteristics and trip chain characteristics of China's urban low-income. Using the travel data collected in the city of Huzhou (a prefecture-level city in east China), Two-Step Clustering Analysis is conducted to identify low- income people from the dataset. It is found that urban low-income people have lower mobility than the non-low-income. They tend to make fewer trips and of shorter distance. However, average trip duration of the low-income is longer than that of the non-low-income. Results show that the low-income individual makes most of their trips for subsistence activities, such as going to work or school. In addition, large proportions of all trips made by the low-income are undertaken by walking, electric motorcycle, and bicycle. The number of trip chain of low-income people is generally small, and the length of said trip chain is short. Finally policy implications are recommended in the context of providing transportation equity service to improve the mobility of urban low-income.

Using the technique of laser flash photolysis, it has been demonstrated that the interaction of SO − 4 • with methionine in aqueous solution at pH 1.0–7.0 results in the formation of the H abstraction radicals Met(-H) · and the dimer of three-electron-bonded radical cations Met − 2 • [S] with maximum absorption at 300 and 480 nm respectively. The characteristic absorption at 480 nm completely disappeared at pH >7.0. The mechanism has been deduced and the kinetic parameters have been determined.