Delivering public value through open government data initiatives in a Smart City context

By using ICT in an innovative way, governments can improve the delivery of services and interaction with stakeholders. Open data is a way to help public organizations became more open and improve interaction with stakeholders. This paper aims to identify what are the public values enhancements acquired on smart city environment that discloses open data. We propose a conceptual model to analyze the smart city initiative. We contextualized the model taking a smart city domain by analyzing three related-initiatives that comprises open data in a smart city case carried at Rio de Janeiro Operations Center (COR) in Brazil by seven deep-interviewees directly involved - from inside and outside – in this case. The findings reveal evidences that open data initiatives contribute to enhance the delivery of public value in smart city contexts.     

A reduction approach to the two-campus transport problem

The two-campus transport problem (TCTP) is a dial-a-ride problem with only two destinations. The problem is motivated by a transport problem between two campuses of an academic college. The two campuses are located in two different cities. Lecturers living in one city are sometimes asked to teach at the other city’s campus. The problem is that of transporting the lecturers from one campus to the other, using a known set of vehicles, so as to minimize the time the lecturers wait for their transport. We mathematically model the general TCTP, and provide an algorithm that solves it, which is polynomial in the number of lecturers. The algorithm is based on a reduction to a shortest path problem.     

Evaluating leadership,IT quality,and net benefits in an e-government environment

US Government organizations are creating value for their citizens and businesses by improving their public service delivery through good websites. Our study examined leadership and IT quality, and their effect on positive delivery outcomes in an e-government environment. We first developed a theoretically based model using elements of the model developed for the Malcolm Baldrige National Quality Award (MBNQA) and DeLone and McLean's IS Success model. To test this model, we conducted a field survey at a municipal city government. The results supported our hypothesis that the MBNQA leadership triad (leadership, strategic planning, and customer/market focus) had a positive impact on the IT quality triad (information, system, and service quality). We also found that both leadership and IT quality increased the benefits.     

Citywide package deliveries via crowdshipping: minimizing the efforts from crowdsourcers

Most current crowdsourced logistics aim to minimize systems cost and maximize delivery capacity, but the efforts of crowdsourcers such as drivers are almost ignored. In the delivery process, drivers usually need to take long-distance detours in hitchhiking rides based package deliveries. In this paper, we propose an approach that integrates offline trajectory data mining and online route-and-schedule optimization in the hitchhiking ride scenario to find optimal delivery routes for packages and drivers. Specifically, we propose a two-phase framework for the delivery route planning and scheduling. In the first phase, the historical trajectory data are mined offline to build the package transport network. In the second phase, we model the delivery route planning and package-taxi matching as an integer linear programming problem and solve it with the Gurobi optimizer. After that, taxis are scheduled to deliver packages with optimal delivery paths via a newly designed scheduling strategy. We evaluate our approach with the real-world datasets; the results show that our proposed approach can complete citywide package deliveries with a high success rate and low extra efforts of taxi drivers.     

Dynamic Accessibility using Big Data: The Role of the Changing Conditions of Network Congestion and Destination Attractiveness

Accessibility is essentially a dynamic concept. However, most studies on urban accessibility take a static approach, overlooking the fact that accessibility conditions change dramatically throughout the day. Due to their high spatial and temporal resolution, the new data sources (Big Data) offer new possibilities for the study of accessibility. The aim of this paper is to analyse urban accessibility considering its two components –the performance of the transport network and the attractiveness of the destinations– using a dynamic approach using data from TomTom and Twitter respectively. This allows us to obtain profiles that highlight the daily variations in accessibility in the city of Madrid, and identify the influence of congestion and the changes in location of the population. These profiles reveal significant variations according to transport zones. Each transport zone has its own accessibility profile, and thus its own specific problems, which require solutions that are also specific.     

考虑随机运能和送到时限的高铁快递运输计划优化方法

为充分利用高铁非高峰期的列车运能,高铁快运已成为铁路快捷货运发展新趋势.然而,受客运需求波动影响,载客动车组可用于高铁快递的运能呈现高度不确定性,给日常运营组织带来巨大挑战.为增强高铁快运系统应对随机运能的能力,提出一种考虑随机运能的高铁快递运输计划优化方法.首先,采用离散场景刻画列车运能的不确定性,以最大化高铁快运期望总收益为目标,综合考虑快递的送到时限和运营约束,针对场景概率分布精确已知情形,构建两阶段随机规划模型(SP);然后,进一步考虑场景概率分布信息部分已知情形,构建两阶段分布鲁棒优化模型(DRO),基于盒式模糊集,借助对偶理论将DRO模型转化为等价的整数线性规划模型并利用GUROBI求解;最后,基于宁杭高铁设计数值实验验证模型的有效性,结果表明,相较于SP模型,DRO模型体现出较强的鲁棒性,只需付出较小的代价即可有效抵抗运能波动对运输计划的影响,并且能够改善最坏情形下解的质量,切实改善高铁快递实际运营中的稳定性.     

Pipelining parallel image compositing and delivery for efficient remote visualization

Scientific datasets of large volumes generated by next-generation computational sciences need to be transferred and processed for remote visualization and distributed collaboration among a geographically dispersed team of scientists. Parallel visualization using high-performance computing facilities is a typical approach to processing such increasingly large datasets. We propose an optimized image compositing scheme with linear pipeline and adaptive transport to support efficient image delivery to a remote client. The proposed scheme arranges an arbitrary number of parallel processors within a cluster in a linear order and divides the image into a carefully selected number of segments, which flow through the linear in-cluster pipeline and wide-area networks to the remote client consecutively. We analytically determine the segment size that minimizes the final image display time and derive the conditions where the proposed image compositing and delivery scheme outperforms the traditional schemes including the binary swap algorithm. In order to match the transport throughput for image delivery over wide-area networks to the pipelining rate for image compositing within the cluster, we design a class of transport protocols using stochastic approximation methods that are able to stabilize the data flow at a target rate. The experimental results from remote visualization of large-scale scientific datasets justify the correctness of our theoretical analysis and illustrate the superior performances of the proposed method.     

DESIGNING ROAD NETWORKS FOR TRANSPORT PLANNING USING EVOLUTIONARY COMPUTATION

In this article, a new approach to designing optimum urban road networks using evolutionary methods is described. The model is capable of finding a road network that addresses the private transport assignment in a certain city area. Experiments using our model in real transport assignment tasks show the promise of the approach to improve existing roads and create new roads as projected in a near future.     

一种动态和自适应公交到站时间预测方法

公交到站时间预测是实现智能化公交信息服务的基础,可靠地预测公交到站时间有利于提高公共交通的服务水平,以吸引更多的城市居民选择公共交通.以某城市公交系统海量的历史数据为基础,建立了基于SVM的集合了静态和动态数据的公交预测模型,该模型引入上游路段速度、下游路段最新速度、下游路段最新花时、时间段和路况拥挤程度等动态信息作为模型特征.在此基础上,根据大量公交到站时间历史数据的波动性,提出了一个基于波动性的自适应预测模型.实验结果表明,自适应预测模型优于现有模型,提高了预测的精确度和效率.     

Virtual 3D City Model for Navigation in Urban Areas

In this paper, we propose to study the integration of a new source of a priori information, which is the virtual 3D city model. We study this integration for two tasks: vehicles geo-localization and obstacles detection. A virtual 3D city model is a realistic representation of the evolution environment of a vehicle. It is a database of geographical and textured 3D data. We describe an ego-localization method that combines measurements of a GPS (Global Positioning System) receiver, odometers, a gyrometer, a video camera and a virtual 3D city model. GPS is often consider as the main sensor for localization of vehicles. But, in urban areas, GPS is not precise or even can be unavailable. So, GPS data are fused with odometers and gyrometer measurements using an Unscented Kalman Filter (UKF). However, during long GPS unavailability, localization with only odometers and gyrometer drift. Thus, we propose a new observation of the location of the vehicle. This observation is based on the matching between the current image acquired by an on-board camera and the virtual 3D city model of the environment. We also propose an obstacle detection method based on the comparison between the image acquired by the on-board camera and the image extracted from the 3D model. The following principle is used: the image acquired by the on-board camera contains the possible dynamic obstacles whereas they are absent from the 3D model. The two proposed concepts are tested on real data.     

Smart Mina: LoRaWAN Technology for Smart Fire Detection Application for Hajj Pilgrimage

The Long-Range Wide Area Network (LoRaWAN) is one of the used communication systems that serve and enables the deployment of the Internet of Things (IoT), which occasionally transmit small size data. As part of the Low Power Wide Area Network (LPWAN), LoRaWAN is characterized by its ability for low power consumption. In addition, it is built to provide more extended coverage and higher capacity with minimum cost. In this paper, we investigate the feasibility and scalability of LoRaWAN for the Mina area using a realistic network model. Mina, known as the world’s largest tent city, is a valley located in the east of Makkah city and surrounded by mountains. It accommodates up to 3 million pilgrims annually and contains more than 100000 tents. The performance was evaluated based on a fire detection-like application. Extensive simulations were conducted using the OMNeT++ simulator and Flora model to determine the delivery ratio, collision, and SF distribution for the simulated scenario with a network consisting of up to ten thousand end devices. The conducted simulations show a promising result for LoRaWAN technology for Mina city. It showed a consistent performance for LoRaWAN in most simulated scenarios when a high success ratio was achieved.     

A hybrid data assimilation scheme for model parameter estimation: Application to morphodynamic modelling

We present a novel algorithm for joint state-parameter estimation using sequential three dimensional variational data assimilation (3D Var) and demonstrate its application in the context of morphodynamic modelling using an idealised two parameter 1D sediment transport model. The new scheme combines a static representation of the state background error covariances with a flow dependent approximation of the state-parameter cross-covariances. For the case presented here, this involves calculating a local finite difference approximation of the gradient of the model with respect to the parameters. The new method is easy to implement and computationally inexpensive to run. Experimental results are positive with the scheme able to recover the model parameters to a high level of accuracy. We expect that there is potential for successful application of this new methodology to larger, more realistic models with more complex parameterisations.     

Predicting residential building age from map data

The age of a building influences its form and fabric composition and this in turn is critical to inferring its energy performance. However, often this data is unknown. In this paper, we present a methodology to automatically identify the construction period of houses, for the purpose of urban energy modelling and simulation. We describe two major stages to achieving this – a per-building classification model and post-classification analysis to improve the accuracy of the class inferences. In the first stage, we extract measures of the morphology and neighbourhood characteristics from readily available topographic mapping, a high-resolution Digital Surface Model and statistical boundary data. These measures are then used as features within a random forest classifier to infer an age category for each building. We evaluate various predictive model combinations based on scenarios of available data, evaluating these using 5-fold cross-validation to train and tune the classifier hyper-parameters based on a sample of city properties. A separate sample estimated the best performing cross-validated model as achieving 77% accuracy. In the second stage, we improve the inferred per-building age classification (for a spatially contiguous neighbourhood test sample) through aggregating prediction probabilities using different methods of spatial reasoning. We report on three methods for achieving this based on adjacency relations, near neighbour graph analysis and graph-cuts label optimisation. We show that post-processing can improve the accuracy by up to 8 percentage points.     

An Analysis Study on Zone-Based Anonymous Communication in Mobile Ad Hoc Networks

A zone-based anonymous positioning routing protocol for ad hoc networks, enabling anonymity of both source and destination, is proposed and analyzed. According to the proposed algorithm, a source sends data to an anonymity zone, where the destination node and a number of other nodes are located. The data is then flooded within the anonymity zone so that a tracer is not able to determine the actual destination node. Source anonymity is also enabled because the positioning routing algorithms do not require the source ID or its position for the correct routing. We develop anonymity protocols for both routeless and route-based data delivery algorithms. To evaluate anonymity, we propose a "measure of anonymity," and we develop an analytical model to evaluate it. By using this model, we perform an extensive analysis of the anonymity protocols to determine the parameters that most impact the anonymity level.     

Optimization of service value

Service organizations try to put more emphasis on strengthening its core competence for customization in order to survive intense competitive pressures. The most difficult thing in customization is that it is difficult to fully understand the service attribute. Moreover, it is not easy to utilize the service attribute in decision making for the service organization. In this study, we try to resolve the uncertainty of the service attribute using the service delivery system. The purpose of this paper is to develop a generic model that optimizes service value from the perspective of the service delivery system. Most studies on service value apply a multidimensional approach based on an empirical model and they measure the service value on the basis of benefits (service quality) and sacrifices (cost). We divide service delivery system into several stages and optimize the service value in terms of service quality and service cost of each stage in one optimization model. We applied our model to a dental service facility in Korea.     

Land‐use dynamic discovery based on heterogeneous mobility sources

Nowadays, cities are the most relevant type of human settlement and their population has been endlessly growing for decades. At the same time, we are witnessing an explosion of digital data that capture many different aspects and details of city life. This allows detecting human mobility patterns in urban areas with more detail than ever before. In this context, based on the fusion of mobility data from different and heterogeneous sources, such as public transport, transport‐network connectivity and Online Social Networks, this study puts forward a novel approach to uncover the actual land use of a city. Unlike previous solutions, our work avoids a time‐invariant approach and it considers the temporal factor based on the assumption that urban areas are not used by citizens all the time in the same manner. We have tested our solution in two different cities showing high accuracy rates.     

Agent-based joint model of residential location choice and real estate price for land use and transport model

Residential location choice (RLC) and real estate price (REP) models are traditional and key components of land use and transport model. In this study, an agent-based joint model of RLC and REP (RLC–REP model) was proposed for SelfSim, an agent-based dynamic evolution of land use and transport model. The RLC–REP model is capable of simulating the negotiation between the active household agents (buyers) and owner agents (sellers) using agent-based modeling. In particular, both utility maximization theory and prospect theory were used to develop a utility function to simulate the location choice behavior of active household agents. The utility function incorporates only two variables: house price and accessibility. The latter variable is calculated using MATSim, an activity-based model. The asking price behavior of owner agents is based on three specific rules. The residential location choices of household agents and house prices can be obtained by negotiation. Finally, genetic algorithm was used to estimate the parameters of the RLC–REP model. The calibrated model was tested in Baoding, a medium-sized city in China, and historical validation was performed to assess its performance. The results suggest that the forecasting ability of the RLC–REP model in terms of real estate price is satisfactory.     

Performance Evaluation of Public Bus Transportation by Using DEA Models and Shannon’s Entropy: An Example From a Company in a Large City of China

The purpose of this paper is to assess the operational efficiency of a public bus transportation via a case study from a company in a large city of China by using data envelopment analysis (DEA) model and Shannon’s entropy. This company operates 37 main routes on the backbone roads. Thus, it plays a significant role in public transportation in the city. According to bus industry norms, an efficiency evaluation index system is constructed from the perspective of both company operations and passenger demands. For passenger satisfaction, passenger waiting time and passenger-crowding degree are considered, and they are undesirable indicators. To describe such indicators, a super-efficient DEA model is constructed. With this model, by using actual data, efficiency is evaluated for each bus route. Results show that the DEA model with Shannon’s entropy being combined achieves more reasonable results. Also, sensitivity analysis is presented. Therefore, the results are meaningful for the company to improve its operations and management.      

Towards a data‐driven IoT software architecture for smart city utilities

The Internet of things (IoT) is emerging as the next big wave of digital presence for billions of devices on the Internet. Smart cities are a practical manifestation of IoT, with the goal of efficient, reliable, and safe delivery of city utilities like water, power, and transport to residents, through their intelligent management. A data‐driven IoT software platform is essential for realizing manageable and sustainable smart utilities and for novel applications to be developed upon them. Here, we propose such service‐oriented software architecture to address 2 key operational activities in a smart utility: the IoT fabric for resource management and the data and application platform for decision‐making. Our design uses Open Web standards and evolving network protocols, cloud and edge resources, and streaming big data platforms. We motivate our design requirements using the smart water management domain; some of these requirements are unique to developing nations. We also validate the architecture within a campus‐scale IoT testbed at the Indian Institute of Science, Bangalore and present our experiences. Our architecture is scalable to a township or city while also generalizable to other smart utility domains. Our experiences serve as a template for other similar efforts, particularly in emerging markets and highlight the gaps and opportunities for a data‐driven IoT software architecture for smart cities.