One of the main challenges of bottom ash reutilization is heavy metal and salts' leaching potential. The effect of wet treatment on chemical composition and leaching toxicity of bottom ash were investigated in this study to mitigate this leaching potential. Batch leaching and column leaching tests were first conducted to investigate the leaching behavior of the targeted elements (Cu, Zn, Ni, Cl-, and SO42-) from raw bottom ash and treated bottom ash after wet treatment. X-ray fluorescence analysis was used to analyze the chemical composition of bottom ash, and sequential extraction procedure (SEP) operation was done to analyze the chemical species of the heavy metals of bottom ash. The obtained results showed that the wet treatment applied on raw bottom ash posed a slight influence on the concentration of most of the major elements, 5.57-18.18%. SEP results showed the acid extractable Zn that accounted for 22.4-24.5% of the total Zn, and the iron manganese oxide bound Ni was 25.2-28.4%, and the organic matter bound Cu was 21.4-31.7%. The wet treatment reduced the concentrations and leachable amount of the targeted pollutants, which could decrease the leaching concentration of Cu by 77.1%, Zn by 34.7%, Ni by 100%, Cl by 30.1%, and SO42- by 51.4% based on the batch leaching tests under acid condition. The column leaching tests also suggest that wet treatment decreases Cu, Zn, and Cl- leaching concentration in bottom ash. This indicates that wet treatment improves the suitability of municipal solid waste incineration bottom ash for reutilization in China.

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This paper focuses on empirically investigating the inertia effects of past behavior in commuting modal shift behavior and contributes to the current state of the art by three aspects. Firstly, this study introduces and tests the potential influences of the inertia effects of past behavior on the traveler’s preferences regarding level-of-service (LOS) variables, besides the impacts of inertia effects on the preference for the frequently used transport mode in the past. Secondly, the mode-specific inertia effects are investigated to distinguish the differences in the inertia effects for different transport modes based on posterior individual-specific parameter estimations. Thirdly, the factors contributing to the heterogeneity of inertia effects including demographics and travel contexts, are quantitatively examined. A joint random parameter logit model using a revealed and stated preference survey regarding commuting behavior is employed to unravel the three aspects. The results reveal significant interactions of inertia terms with LOS variables indicating the influences of past behavior on travelers’ evaluations on attributes of their previous choices. The mean values and variances of inertia effects for different transport modes are significantly and substantially distinct. For instance, the inertia effects of frequently using car are substantially positive representing strong stickiness to the car, while the inertia effects of frequently using the metro have large variances among travelers and mostly appear as dispositions to change. Besides, the effects of personal characteristics and travel contexts on the magnitude of the inertia effects of different transport modes are identified as well. A demand estimation analysis is utilized to investigate the influences of three aspects on predicting travel demands in various contexts. Incorporating the interactions and mode-specific inertia effects can remarkably improve the model performance. The demand estimation will be biased if they are neglected.

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People are engaged in a variety of activities through space every day. The choice of type and location of activities is known as human spatial behavior. Urban decision makers need to understand how land use and transportation systems can shape human spatial behavior in order to design better systems. In the past, they have collected mobility data through travel surveys to understand human spatial behavior. Today, a wide range of automatically collected data have become available as alternative data sources. Big mobility data vs. traditional travel survey data has been a topic of long-time debate in human mobility and travel behavior research. Big data are intuitively better but this is not always the case. Big mobility data relate to a large number of travelers and trips but little is known about each individual individual traveler and trip, not to mention that sometimes their information has to be aggregated for privacy concerns. On the other hand, travel survey data, despite reporting only a small group of respondents, tend to include abundant features about each individual traveler, such as age and attitudes, and each trip, such as trip purpose. Assuming that each row represents one traveler and each column represents one feature, big mobility data should have been described as long and thin, and “small” survey data (Chen et al., 2016) as short and wide.@en

In a multimodal public transport network, transfers are inevitable. Planning and managing an efficient transfer connection is thus important and requires an understanding of the factors that influence those transfers. Existing studies on predicting passenger transfer flows have mainly used transit assignment models based on route choice, which need extensive computation and underlying behavioral assumptions. Inspired by studies that use network properties to estimate public transport (PT) demand, this paper proposes to use the network properties of a multimodal PT system to explain transfer flows. A statistical model is estimated to identify the relationship between transfer flow and the network properties in a joint bus and metro network. Apart from transfer time, the number of stops, and bus lines, the most important network property we propose in this study is transfer accessibility. Transfer accessibility is a newly defined indicator for the geographic factors contributing to the possibility of transferring at a station, given its position in a multimodal PT network, based on an adapted gravity-based measure. It assumes that transfer accessibility at each station is proportional to the number of reachable points of interest within the network and dependent on a cost function describing the effect of distance. The R-squared of the regression model we propose is 0.69, based on the smart card data, PT network data, and Points of Interest (POIs) data from the city of Beijing, China. This suggests that the model could offer some decision support for PT planners especially when complex network assignment models are too computationally intensive to calibrate and use.

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Almost everyone has a mobile phone today. In addition to calls and text messages, people are utilizing mobile apps and websites to connect to the world and explore different content anytime and anywhere. The use of smart phones generates billions of records, including spatiotemporal trajectories, and various mobile phone usage details, such as call duration, and frequency of visiting a certain type of website. Most transportation researchers have only focused on spatiotemporal traces, which represent activity-travel behavior of users. However, it is worth making full use of smart phone data to study how mobile phone usage is related to activity-travel behavior. This chapter first reviews the existing literature on the relevant topics to demonstrate the lack of research on the relationship between mobile internet usage and activity-travel behavior. Based on an 11-day dataset from Shanghai that includes not only spatiotemporal traces but also the frequencies of browsing different categories of mobile internet content (e.g., tourism and finance), we examine several relationships between mobile internet usage and activity-travel behavior.@en

New mobility data sources like mobile phone traces have been shown to reveal individuals’ movements in space and time. However, socioeconomic attributes of travellers are missing in those data. Consequently, it is not possible to partition the population and have an in-depth understanding of the socio-demographic factors influencing travel behaviour. Aiming at filling this gap, we use mobile internet usage behaviour, including one's preferred type of website and application (app) visited through mobile internet as well as the level of usage frequency, as a distinguishing element between different population segments. We compare the travel behaviour of each segment in terms of the preference for types of trip destinations. The point of interest (POI) data are used to cluster grid cells of a city according to the main function of a grid cell, serving as a reference to determine the type of trip destination. The method is tested for the city of Shanghai, China, by using a special mobile phone dataset that includes not only the spatial-temporal traces but also the mobile internet usage behaviour of the same users. We identify statistically significant relationships between a traveller's favourite category of mobile internet content and more frequent types of trip destinations that he/she visits. For example, compared to others, people whose favourite type of app/website is in the “tourism” category significantly preferred to visit touristy areas. Moreover, users with different levels of internet usage intensity show different preferences for types of destinations as well. We found that people who used mobile internet more intensively were more likely to visit more commercial areas, and people who used it less preferred to have activities in predominantly residential areas.

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This study uses mobile phone data to understand mobility patterns in a country, with limited mobility data, in order to give advice about decisions on how to design the national and regional road network. Our method consists of three parts: (1) filtering mobile phone traces to derive mobility patterns, (2) building an adapted formulation of the gravity-based trip distribution model, which considers telecommunication intensity (i.e., aggregate number of calls and text messages) and travel time as input to forecast the influence of road improvements on country-wide mobility, and (3) optimizing the road network investment based on the adapted trip distribution model by using a local search algorithm. The method was applied to the case study country of Senegal. The mobile phone data was transformed to support informed decisions on road network development in that country given different objectives, namely accessibility and equity. We believe that the methodology is valuable and reproducible to other countries where traditional mobility data is scarce but mobile phone data is available to transport planners.@en

This paper proposes to segment the population using mobile internet usage data and accordingly to better understand travellers’ preferences for different types of trip destination revealed in mobile phone traces. The method is tested for the city of Shanghai, China, by using a special mobile phone dataset that includes not only the spatial-temporal traces but also the mobile internet usage data of the same users. We identify statistically significant relationships between a traveller’s favourite category of mobile internet content and more frequent types of trip destinations that he/she visits. For example, compared to others, people who preferred the content category “tourism”, had a significantly higher preference to visit touristy areas. Moreover, people who used mobile internet more intensively were more likely to visit more commercial areas, and people who used less mobile internet preferred to have activities in predominantly residential areas.@en

A location choice model explains how travellers choose their trip destinations especially for those activities which are flexible in space and time. The model is usually estimated using travel survey data; however, little is known about how to use smart card data (SCD) for this purpose in a public transport network. Our study extracted trip information from SCD to model location choice of after-work activities. We newly defined the metrics of travel impedance in this case. Moreover, since socio-demographic information is missing in such anonymous data, we used observable proxy indicators, including commuting distance and the characteristics of one's home and workplace stations, to capture some interpersonal heterogeneity. Such heterogeneity is expected to distinguish the population and better explain the difference of their location choice behaviour. The approach was applied to metro travellers in the city of Shanghai, China. As a result, the model performs well in explaining the choices. Our new metrics of travel impedance to access an after-work activity result in a better model fit than the existing metrics and add additional interpretability to the results. Moreover, the proxy variables distinguishing the population seem to influence the choice behaviour and thus improve the model performance.

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