Trip Chaining Research Articles

Combinatorial optimization of construction waste collection and transportation: A case study of Hong Kong

Although construction waste consistently contributes the highest proportion of all solid waste landfilled, its collection and transportation has received little attention. This research conducts a case study of Hong Kong with the aim of unraveling the causes of ineffective construction waste management from a logistics perspective and developing strategies to improve efficiency of waste collection and transportation. We analyze 112,892 individual trips undertaken by 2,563 construction waste hauling trucks and find three critical issues, namely irrational facility choice, disorganized trip chains, and serious underloading. We design an order-to-order distance matrix, based on which we develop a combinatorial approach to optimizing construction waste collection and transportation through (1) optimal facility choice, (2) proper order sequencing, and (3) increased loading ratio. Simulation results indicate that optimal facility choice is most effective of the three strategies, reducing travel distance by 15.2% (15,256 km) and saving 15.3% (21,467 kg) in CO2-eq emissions. Combining the three strategies creates the best optimization effects, saving 20.3% of travel distance (20,346 km) and 18.2% of CO2-eq emissions (25,544 kg). Our findings provide valuable insights for construction waste management and suggest strategies (e.g., developing a work dispatch system like Uber or proper vehicle routing algorithms) for improving waste collection efficiency and reducing carbon emissions.

Individual, household, and urban form determinants of trip chaining of non-work travel in México City

This project studies the driving factors of non-work travel trip chaining in the México City Metropolitan Area (MCMA). It has been argued that urban traveling is increasingly becoming more intricate given that travelers pursue more than one activity in their daily travel. Unlike commuting, non-work travel has been less studied, the literature being even scarcer if we consider the Latin American context as well. Non-work travel is represented by travel to study and other variety of destinations. Thus, using the 2017 Household Origin-Destination Survey (HODS17), this paper analyzes separately the groups of “Students” and the rest of travelers called “Others” in terms of patterns of trip chaining among three modes choices: Transit, Car, and Mixed transportation. Two regression models were applied to study the determinants of 1) number of stops in the daily travel (trip chaining) and 2) tour categories in increasing degree of complexity. A descriptive analysis identified that Car users and Mixed transportation had the highest shares of complex tours together with those in the “Others” group. The associations found between some socioeconomic variables (gender, age, and household size) with trip chaining and tour complexity were different among Students and Others, and discussed in relation to the existing literature. Moreover, most urban form variables supported the hypothesis that dense and diverse urban environments are associated with more trip chaining and tour complexity. These results were discussed in light of the implications to understand the transport disadvantage condition of peripheral low-income neighborhoods typical of big cities in the developing context.

Electric vehicle charging station planning with dynamic prediction of elastic charging demand: a hybrid particle swarm optimization algorithm

This paper is concerned with the electric vehicle (EV) charging station planning problem based on the dynamic charging demand. Considering the dynamic charging behavior of EV users, a dynamic prediction method of EV charging demand is proposed by analyzing EV users’ travel law via the trip chain approach. In addition, a multi-objective charging station planing problem is formulated to achieve three objectives: (1) maximize the captured charging demands; (2) minimize the total cost of electricity and the time consumed for charging; and (3) minimize the load variance of the power grid. To solve such a problem, a novel method is proposed by combining the hybrid particle swarm optimization (HPSO) algorithm with the entropy-based technique for order preference by similarity to ideal solution (ETOPSIS) method. Specifically, the HPSO algorithm is used to obtain the Pareto solutions, and the ETOPSIS method is employed to determine the optimal scheme. Based on the proposed method, the siting and sizing of the EV charging station can be planned in an optimal way. Finally, the effectiveness of the proposed method is verified via the case study based on a test system composed of an IEEE 33-node distribution system and a 33-node traffic network system.

Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data

Observing and analyzing travel behavior is important, requiring understanding detailed individual trip chains. Existing studies on identifying travel modes have mainly used some travel features based on GPS and survey data from a small number of users. However, few studies have focused on evaluating the effectiveness of these models on large-scale location data. This paper proposes to use travel location data from an Internet company and travel data from transport department to identify travel modes. A multiple binary classification model based on data fusion is used to find out the relationship between travel mode and different features. Firstly, we enlisted volunteers to collect travel data and record their travel trip process using a custom-developed WeChat program. Secondly, we have developed three binary classification models to explain how different attributes can be used to model travel mode. Compared with one multi-classification model, the accuracy of our model improved significantly, with prediction accuracies of 0.839, 0.899, 0.742, 0.799, and 0.799 for walk, metro, bike, bus, and car, respectively. This suggests that the model could be applied not only in engineering practice to identify the trip chain from Internet location data but also in decision support for transportation planners.

A Framework for Dynamic Advanced Traveler Information Systems

This paper presents the framework for a dynamic Advanced Traveler Information System (ATIS). The ATIS currently in use provides users with stereotyped travel options, but the set of available modes in a given place and time is not the same for each traveler, and such a personal choice set varies within the context of daily trip chains. The research presented in this paper addressed these limitations by including dynamic features in the proposed system. The activity chain that the user performs as well as the personal mode availabilities are modelled simultaneously to define the logical architecture of an innovative information system. Such a technology was intended to assist travelers in performing their daily trip chaining. In order to provide some insight regarding the efficacy of the proposed procedure, a pilot test was performed using real travel time information. Results have shown that the ATIS proposed in this study might generate a significant reduction in travel times.

Modeling the Trip Distributions of Tourists Based on Trip Chain and Entropy-Maximizing Theory

Suburban tourist railway is an emerging transportation mode for tourism. Knowing the travel demand and trip distribution patterns of tourists is an important prerequisite to the planning and construction of suburban tourist railways. However, this issue has attracted very little research attention so far. Therefore, this paper proposes a forecasting model focused on the trip distribution of tourists who travel with the suburban tourist railway. Based on the analysis of the characteristics of tourists’ trips and the use of the trip chain method, the frequency, order, distance, and visiting volume of stay points of the trips of tourists have been intensively studied. Then, a tourist trip distribution forecasting model was built in this paper. It uses the Entropy-Maximizing theory to predict trip chain distribution probability and obtain the distribution of tourists within the city. A case study that takes the H city as an example was conducted to test the proposed model. The results of this case show that the output of the model can reflect the real trip distribution characteristics of tourists very well, which demonstrates the applicability and effectiveness of the proposed model.

Measuring positive public transit accessibility using big transit data

ABSTRACT Most of the current existing accessibility measures quantify the potential of reaching desirable opportunities across space and time. Nevertheless, these potential measurements only illustrate the maximum possible accessibility a person can have, which may not accurately measure real-world transit accessibility in urban areas. This paper introduces a novel methodology to measure positive public transit accessibility based on multi-source big public transit data such as Smart Card Data (SCD) and Global Navigation Satellite System trajectory data, which embed rich travel information and real-world spatio-temporal constraints. First, we use multi-source transit data to reconstruct trip chains, which are used to extract popular destinations. A novel transit accessibility measure is defined to account for latent trip information such as mode/route preference, opportunity attraction, and travel impedance that are difficult to capture explicitly via traditional normative measures. Finally, we produce accessibility maps to visualize time-varying and heterogeneous accessibility patterns distributed over the study region. We performed an empirical evaluation on real-world transit data collected in Shenzhen City, China, demonstrating the applicability and effectiveness of the proposed method in mapping positive transit accessibility over large metropolitan areas. The results and findings of the empirical study demonstrate that the proposed positive accessibility measure can better capture travel behavior characteristics and constraints than traditional normative measures. The measurement method can be used as a practical high-resolution mapping tool for transit decision makers in evaluating public transit systems, supporting strategic transit planning, and improving daily transit management.

Nodal charging demand forecast of EVs considering drivers’ psychological bearing ability based on NMC‐MCS

Widespread adoption of electric vehicles (EVs) would significantly increase the electrical load demand in power distribution networks. Most previous studies investigated EV charging demand based on drivers’ trip habits, but the impact of psychological bearing ability (PBA) about the range anxiety on EV drivers’ charging decision are ignored. Here a novel forecast method considering drivers’ PBA for predicting nodal charging demand of EVs is proposed. The charging decision model considering PBA is established based on an improved Richards model, and the spatial-temporal dynamics model is established based on the non-homogeneous Markov chain (NMC) and random trip chain. Meanwhile, the Monte Carlo simulation (MCS) is adopted to avoid the disaster of dimensionality in large scale EVs charging problem. The proposed method is illustrated by an actual system integrated traffic network and power grid. The simulation results demonstrate that drivers’ PBA will significantly affect the charging decision, then changes the spatial-temporal distribution of charging power demand. The conclusion is that the drivers with lower PBA have a higher charging demand, and the impact of drivers’ PBA on charging power has a close relation with the initial battery level (IBL) of EVs.

Optimal planning of intra-city public charging stations

Intra-city Public Charging Stations (PCSs) play a crucial role in promoting the mass deployment of Electric Vehicles (EVs). To motivate the investment on PCSs, this work proposes a novel framework to find the optimal location and size of PCSs, which can maximize the benefit of the investment. The impacts of charging behaviors and urban land uses on the income of PCSs are taken into account. An agent-based trip chain model is used to represent the travel and charging patterns of EV owners. A cell-based geographic partition method based on Geographic Information System is employed to reflect the influence of land use on the dynamic and stochastic nature of EV charging behaviors. Based on the distributed charging demand, the optimal location and size of PCSs are determined by mixed-integer linear programming. Västerås, a Swedish city, is used as a case study to demonstrate the model's effectiveness. It is found that the charging demand served by a PCS is critical to its profitability, which is greatly affected by the charging behavior of drivers, the location and the service range of PCS. Moreover, charging price is another significant factor impacting profitability, and consequently the competitiveness of slow and fast PCSs.

Charging Strategy for Electric Vehicles Considering Consumer Psychology and Trip Chain

As a kind of movable storage device, the electrical vehicles (EVs) are able to support load shaving through orderly charging. The existing researches mostly focus on the design of EVs charging control technology with little consideration of trip-chain-based consumer psychology of EV owners. To fill this gap, this article proposes a price-based orderly charging strategy for EVs considering both consumer psychology and trip chain. Then, the load shaving problem is transformed into a multiobjective optimization problem, to minimize peak-to-valley difference and network loss. A time-of-use price optimization model based on consumer psychology is established to describe the charging behavior of EV owners influenced by electricity price. Finally, the examples verify the feasibility of the proposed strategy by comparing the impact of EVs connected to grid under different ratios, different load transfer rates, and different scenarios.

Impacts of online shopping on travel demand: a systematic review

ABSTRACT The rise of e-commerce has led to substantial changes in personal travel and activities. We systematically reviewed empirical studies on the relationship between online shopping and personal travel behaviour. We synthesised and assessed the evidence for four types of effects on various travel outcomes, including trip frequency, travel distance, trip chaining, mode choice, and time use. In 42 articles reviewed, we found more evidence that online shopping substitutes for shopping travel. Most studies to date have focused on trip frequency but neglected other travel outcomes. Very few studies have considered the modification effect, which has significant implications for travel demand management. In sum, previous studies have not reached a consensus on the dominant effect of online shopping, in part due to the diversity in variable measurements, types of goods, study areas, and analytic methods. A limitation of previous studies is the reliance on cross-sectional surveys, which hinders the distinction between short- and long-term behaviours and between modification, complementarity, and substitution effects. Our study provides an agenda for future research on this topic and discusses policy implications related to land use, behavioural changes, data collection, and modelling for practitioners who wish to incorporate e-commerce in planning for sustainable urban systems.

Assessing polycentric development in terms of trip chaining efficiency

Polycentric urban growth with dense, diverse, and accessible centers is assumed to have multiple economic, social, and environmental benefits for the region. Yet, evidence on the benefits of polycentricity in the transportation sector remains less studied, and previous research has found mixed results. Besides, to our knowledge, none of the previous research focuses on trip chains or tours (sequence of trips that begins and ends at home) in centers. This paper investigates trip chaining efficiency in 28 U.S. regions, comparing three different types of tours, i.e., tours that are within a center, tours that are partially inside a center, and tours that are completely outside a center. I consider a tour to be efficient if it comprises travel modes other than the personal automobile. The results of parametric tests show that tours within a center are associated with higher walk, bike, and transit mode shares and lower car share. Effect sizes are larger for walk and car shares than for bike and transit shares. The findings of this research can encourage and guide planners to design environments that promote active transportation while maximizing resident access to services and amenities and minimizing their expenditure on mobility and travel time.

Impact of Large-Scale Mobile Electric Vehicle Charging in Smart Grids: A Reliability Perspective

The charging load of electric vehicles (EVs) is characterized by uncertainty and flexibility, which burdens the distribution network, especially when there is a high penetration of distributed generation (DG) in smart grids. Large-scale EV mobility integration not only affects smart grid operation reliability but also the reliability of EV charging services. This paper aims at estimating the comprehensive impacts caused by spatial-temporal EV charging from the perspective of both electricity system reliability and EV charging service reliability. First, a comprehensive reliability index system, including two novel indexes quantifying EV charging service reliability, is proposed. Then, considering traffic constraints and users’ charging willingness, a spatial-temporal charging load model is introduced. In the coupled transportation and grid framework, the reliability impacts from plenty of operation factors are analyzed. Moreover, the electricity system reliability and EV charging service reliability correlated with DG integration are discussed. A coupled transportation grid system is adopted to demonstrate the effectiveness and practicability of the proposed method. The numerical results analyze reliability impacts from EV penetration level, trip chain, EV battery capacity, DG installation location, and capacity. The proposed studies reveal that when the EV capacity ratio to DG capacity is 3:1, the system reliability reaches the maximum level.

Trip generation, trip chains and polycentric development in metropolitan USA: A Case Study of the Wasatch Front Region, Utah

Polycentric development is being promoted to lessen problems of urban sprawl and reduce vehicle miles traveled (VMT). This study examines polycentric development in the Wasatch Front Region (WFR), Utah, from a morphological perspective using employment data and a functional perspective by analyzing trip chain behavior. We find that morphologically, the WFR is more dispersed than centered but only has one functional urban center as the downtown Salt Lake City. Regarding trip chains, we find that although the automobile is the dominant travel mode outside urban centers, the trip chains by walking and biking are common in urban centers. Furthermore, we employed a multinomial logit model to examine travel behavior associated with urban centers. The results suggest that the WFR is still dominated by a single urban center, rather than being polycentric, reflecting the reality of many U.S. metropolitan regions. Our study suggests that job opportunities and amenities are critical to increasing functional polycentricity. Moreover, compact development, open space, and walkability encourage trip chain generation in urban centers. Last, improved public transportation facilities in the urban centers provide more options for commuting beyond the automobile and are necessary to increase the functional polycentricity of urban centers.

Estimating Express Train Preference of Urban Railway Passengers Based on Extreme Gradient Boosting (XGBoost) using Smart Card Data

As the share of public transport increases, the express strategy of the urban railway is regarded as one of the solutions that allow the public transportation system to operate efficiently. It is crucial to express the urban railway’s express strategy to balance a passenger load between the two types of trains, that is, local and express trains. This research aims to estimate passengers’ preference between local and express trains based on a machine learning technique. Extreme gradient boosting (XGBoost) is trained to model express train preference using smart card and train log data. The passengers are categorized into four types according to their preference for the local and express trains. The smart card data and train log data of Metro Line 9 in Seoul are combined to generate the individual trip chain alternatives for each passenger. With the dataset, the train preference is estimated by XGBoost, and Shapley additive explanations (SHAP) is used to interpret and analyze the importance of individual features. The overall F1 score of the model is estimated to be 0.982. The results of feature analysis show that the total travel time of the local train feature is found to substantially affect the probability of express train preference with a 1.871 SHAP value. As a result, the probability of the express train preference increases with longer total travel time, shorter in-vehicle time, shorter waiting time, and few transfers on the passenger’s route. The model shows notable performance in accuracy and provided an understanding of the estimation results.

Estimation of walking patterns in a touristic area with Wi-Fi packet sensors

We propose a methodology for using the records from Wi-Fi packet sensors to model route choice and time spent at different locations within a tourist area. In contrast to other route choice problems, in tourist areas it is often the case that “the route is the goal,” so we expect route choice behavior to be less destination oriented. Data were collected from Higashiyama Ward, Kyoto, Japan, which is highly frequented by tourists. We utilized Wi-Fi packet sensors, which avoid the need for mobile applications but provide only spot data. Based on the data, we first discuss the extraction of tourist trip chains and the construction of a simplified time-space network with “stay” and “move” links. The network and link attributes are based on the location of the sensors as well as map data. Recursive Logit (RL) models are then employed to formulate tourists’ route choice behavior. The existence of different kinds of “points of interests” (POIs) is shown to explain route choice as well as the time spent on roads with shops, restaurants and sightseeing spots.

Regulation Ability Estimation of Private EVs at City Level Considering Users’ Trip Chain and Regulation Willingness

Regulation Ability Estimation of Private EVs at City Level Considering Users’ Trip Chain and Regulation Willingness

Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China

As the propensity to link multiple intermediate stops in a trip chain (a sequence of journeys that starts and ends at home, includes visiting one or more locations) is more prevalent, the relationship between travel mode choice and trip chain pattern aroused the attention of academics. This paper examines two distinct structures to identify the decision process of travelers between travel mode choice and trip chain pattern: one structure in which trip chain pattern organization precedes travel mode choice, another structure in which travel mode choice decision precedes the organization of trip chain pattern. To accommodate multi-day behavioral variability and unobserved heterogeneity in personal characteristics ignored by traditional travel surveys, multi-day GPS data collected in Shanghai is employed to estimate these two structures within Nested Logit (NL) model. The Monte Carlo (MC) method simulates the switch of trip-chaining and mode choice under possible Transportation Demand Management (TDM) strategies based on estimation results. The findings of this study are as follows: (1) trip chain pattern decision precedes travel mode choice, which means trip-chaining is organized first and affects travel mode choice; (2) complex trip chain is related to higher automobile dependency, and it is a barrier to the tendency to adopt public transit; (3) people who generally travel by automobiles might switch to public transit when private cars are unavailable, and an increase in household bicycle ownership enhances competition between the bicycle and public transit which leads people to turn to cycling. These findings help implement TDM strategies to develop sustainable transportation systems and optimize the urban trip structure.

Using Naturalistic Driving Data to Predict Mild Cognitive Impairment and Dementia: Preliminary Findings from the Longitudinal Research on Aging Drivers (LongROAD) Study.

Emerging evidence suggests that atypical changes in driving behaviors may be early signals of mild cognitive impairment (MCI) and dementia. This study aims to assess the utility of naturalistic driving data and machine learning techniques in predicting incident MCI and dementia in older adults. Monthly driving data captured by in-vehicle recording devices for up to 45 months from 2977 participants of the Longitudinal Research on Aging Drivers study were processed to generate 29 variables measuring driving behaviors, space and performance. Incident MCI and dementia cases (n = 64) were ascertained from medical record reviews and annual interviews. Random forests were used to classify the participant MCI/dementia status during the follow-up. The F1 score of random forests in discriminating MCI/dementia status was 29% based on demographic characteristics (age, sex, race/ethnicity and education) only, 66% based on driving variables only, and 88% based on demographic characteristics and driving variables. Feature importance analysis revealed that age was most predictive of MCI and dementia, followed by the percentage of trips traveled within 15 miles of home, race/ethnicity, minutes per trip chain (i.e., length of trips starting and ending at home), minutes per trip, and number of hard braking events with deceleration rates ≥ 0.35 g. If validated, the algorithms developed in this study could provide a novel tool for early detection and management of MCI and dementia in older drivers.

Impacts of School Reopening on Variations in Local Bus Performance in Sydney

During the COVID-19 pandemic, stay-at-home orders in conjunction with working from home, school closures, and event cancellations resulted in a decrease in travel demand. Under normal circumstances, these activities are components of trip chains and utilize a multimodal transport network. The overall performance of the network can be traced through delays in the bus system as buses capture both changes in ridership and fluctuations in mixed traffic conditions. This paper explores the hypothesis that resumption of a single component in trip chains (i.e., school reopening) is sufficient for a measurable change in transport system performance. This study used school reopening in Sydney, Australia as a case study to explore whether school-related trips affected bus system performance directly with higher student patronage or indirectly with heavier road congestion from parental car trips. Both stop dwell times and differences in delays between successive stops were used as bus service indicators. Dwell times reflect the travel demand for buses and delay differences capture local changes in service reliability. We found that increase in ridership had limited impacts on bus punctuality. However, the level of local bus performance worsened after schools reopened, and the effect was more pronounced in commercial areas in the afternoon when schools ended, suggesting secondary trip purposes such as leisure and shopping in addition to school pick-ups. This study revealed the interaction between different trip purposes during the postshutdown period and threw light on changes in travel behavior patterns as travel restrictions were relaxed in pandemic circumstances.