Exploring the operational performance discrepancies between online ridesplitting and carpooling transportation modes based on DiDi data View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2022-06-17

AUTHORS

Haoran Chen, Xuedong Yan, Xiaobing Liu, Tao Ma

ABSTRACT

With the popularization of Internet technologies and shared mobility services, online ridesharing has developed rapidly in numerous cities worldwide. However, perhaps owing to the lack of empirical data, there is a lack of comprehensive and comparative studies on the two major online ridesharing modes, namely, ridesplitting and carpooling, vis-à-vis operational performance discrepancies. Thus, we conduct an empirical study using the massive amount of actual operating data provided by DiDi Chuxing. Based on an analysis of the operating characteristics of ridesplitting and carpooling, this study proposes an approach to estimate ridesharing fuel-saving and distance-saving performance by combining the vehicle operating information and fuel economy indicators of various transportation modes. Furthermore, the operational performance discrepancies between the two major ridesharing modes are compared through an analysis of the user characteristics and interactive effects between ridesharing and subway systems. The results show that the average fuel-saving and distance-saving ratios of ridesplitting are lower than those of carpooling. From the perspective of the transportation system’s fuel economy, ridesharing is not considered to be fuel-saving, and its scale should be reasonably regulated. According to driver classification, carpooling is more suitable for commuting and intercity transportation. In addition, ridesplitting and carpooling can be employed as feeders into subway networks in suburban areas. These findings are believed likely to be beneficial for facilitating the sustainable and standardized development of these two ridesharing modes. More... »

PAGES

1-36

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11116-022-10297-6

DOI

http://dx.doi.org/10.1007/s11116-022-10297-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1148761123


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/12", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Built Environment and Design", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/15", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Commerce, Management, Tourism and Services", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0905", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Civil Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1205", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Urban and Regional Planning", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1507", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Transportation and Freight Services", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, 100044, Beijing, People\u2019s Republic of China", 
          "id": "http://www.grid.ac/institutes/grid.181531.f", 
          "name": [
            "MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, 100044, Beijing, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Haoran", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, 100044, Beijing, People\u2019s Republic of China", 
          "id": "http://www.grid.ac/institutes/grid.181531.f", 
          "name": [
            "MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, 100044, Beijing, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yan", 
        "givenName": "Xuedong", 
        "id": "sg:person.01014536560.46", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01014536560.46"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, 100044, Beijing, People\u2019s Republic of China", 
          "id": "http://www.grid.ac/institutes/grid.181531.f", 
          "name": [
            "MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, 100044, Beijing, People\u2019s Republic of China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Xiaobing", 
        "id": "sg:person.016651031157.51", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016651031157.51"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Civil, Geo and Environmental Engineering, Technical University of Munich, Arcisstrasse 21, 80333, Munich, Germany", 
          "id": "http://www.grid.ac/institutes/grid.6936.a", 
          "name": [
            "Department of Civil, Geo and Environmental Engineering, Technical University of Munich, Arcisstrasse 21, 80333, Munich, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ma", 
        "givenName": "Tao", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/ncomms10793", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009219500", 
          "https://doi.org/10.1038/ncomms10793"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s11116-014-9531-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051706399", 
          "https://doi.org/10.1007/s11116-014-9531-8"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2022-06-17", 
    "datePublishedReg": "2022-06-17", 
    "description": "With the popularization of Internet technologies and shared mobility services, online ridesharing has developed rapidly in numerous cities worldwide. However, perhaps owing to the lack of empirical data, there is a lack of comprehensive and comparative studies on the two major online ridesharing modes, namely, ridesplitting and carpooling, vis-\u00e0-vis operational performance discrepancies. Thus, we conduct an empirical study using the massive amount of actual operating data provided by DiDi Chuxing. Based on an analysis of the operating characteristics of ridesplitting and carpooling, this study proposes an approach to estimate ridesharing fuel-saving and distance-saving performance by combining the vehicle operating information and fuel economy indicators of various transportation modes. Furthermore, the operational performance discrepancies between the two major ridesharing modes are compared through an analysis of the user characteristics and interactive effects between ridesharing and subway systems. The results show that the average fuel-saving and distance-saving ratios of ridesplitting are lower than those of carpooling. From the perspective of the transportation system\u2019s fuel economy, ridesharing is not considered to be fuel-saving, and its scale should be reasonably regulated. According to driver classification, carpooling is more suitable for commuting and intercity transportation. In addition, ridesplitting and carpooling can be employed as feeders into subway networks in suburban areas. These findings are believed likely to be beneficial for facilitating the sustainable and standardized development of these two ridesharing modes.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s11116-022-10297-6", 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.8324248", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.8374558", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1043848", 
        "issn": [
          "0049-4488", 
          "1572-9435"
        ], 
        "name": "Transportation", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }
    ], 
    "keywords": [
      "Internet technology", 
      "massive amounts", 
      "mobility services", 
      "driver classification", 
      "user characteristics", 
      "performance discrepancy", 
      "transportation modes", 
      "Didi Chuxing", 
      "ridesharing", 
      "carpooling", 
      "subway network", 
      "operating data", 
      "actual operating data", 
      "standardized development", 
      "empirical study", 
      "network", 
      "subway system", 
      "popularization", 
      "services", 
      "classification", 
      "technology", 
      "information", 
      "data", 
      "intercity transportation", 
      "vehicles", 
      "numerous cities", 
      "empirical data", 
      "performance", 
      "system", 
      "transportation", 
      "fuel economy", 
      "comparative study", 
      "lack", 
      "economy indicators", 
      "mode", 
      "characteristics", 
      "amount", 
      "analysis", 
      "perspective", 
      "development", 
      "results", 
      "city", 
      "area", 
      "feeders", 
      "addition", 
      "economy", 
      "scale", 
      "suburban areas", 
      "discrepancy", 
      "indicators", 
      "study", 
      "commuting", 
      "ratio", 
      "findings", 
      "approach", 
      "effect", 
      "interactive effects", 
      "ridesplitting"
    ], 
    "name": "Exploring the operational performance discrepancies between online ridesplitting and carpooling transportation modes based on DiDi data", 
    "pagination": "1-36", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1148761123"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s11116-022-10297-6"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s11116-022-10297-6", 
      "https://app.dimensions.ai/details/publication/pub.1148761123"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-09-02T16:07", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220902/entities/gbq_results/article/article_927.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s11116-022-10297-6"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11116-022-10297-6'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11116-022-10297-6'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11116-022-10297-6'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11116-022-10297-6'


 

This table displays all metadata directly associated to this object as RDF triples.

159 TRIPLES      21 PREDICATES      86 URIs      72 LITERALS      4 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s11116-022-10297-6 schema:about anzsrc-for:09
2 anzsrc-for:0905
3 anzsrc-for:12
4 anzsrc-for:1205
5 anzsrc-for:15
6 anzsrc-for:1507
7 schema:author Nc1143fc449fa43bf90044956548b7a13
8 schema:citation sg:pub.10.1007/s11116-014-9531-8
9 sg:pub.10.1038/ncomms10793
10 schema:datePublished 2022-06-17
11 schema:datePublishedReg 2022-06-17
12 schema:description With the popularization of Internet technologies and shared mobility services, online ridesharing has developed rapidly in numerous cities worldwide. However, perhaps owing to the lack of empirical data, there is a lack of comprehensive and comparative studies on the two major online ridesharing modes, namely, ridesplitting and carpooling, vis-à-vis operational performance discrepancies. Thus, we conduct an empirical study using the massive amount of actual operating data provided by DiDi Chuxing. Based on an analysis of the operating characteristics of ridesplitting and carpooling, this study proposes an approach to estimate ridesharing fuel-saving and distance-saving performance by combining the vehicle operating information and fuel economy indicators of various transportation modes. Furthermore, the operational performance discrepancies between the two major ridesharing modes are compared through an analysis of the user characteristics and interactive effects between ridesharing and subway systems. The results show that the average fuel-saving and distance-saving ratios of ridesplitting are lower than those of carpooling. From the perspective of the transportation system’s fuel economy, ridesharing is not considered to be fuel-saving, and its scale should be reasonably regulated. According to driver classification, carpooling is more suitable for commuting and intercity transportation. In addition, ridesplitting and carpooling can be employed as feeders into subway networks in suburban areas. These findings are believed likely to be beneficial for facilitating the sustainable and standardized development of these two ridesharing modes.
13 schema:genre article
14 schema:isAccessibleForFree false
15 schema:isPartOf sg:journal.1043848
16 schema:keywords Didi Chuxing
17 Internet technology
18 actual operating data
19 addition
20 amount
21 analysis
22 approach
23 area
24 carpooling
25 characteristics
26 city
27 classification
28 commuting
29 comparative study
30 data
31 development
32 discrepancy
33 driver classification
34 economy
35 economy indicators
36 effect
37 empirical data
38 empirical study
39 feeders
40 findings
41 fuel economy
42 indicators
43 information
44 interactive effects
45 intercity transportation
46 lack
47 massive amounts
48 mobility services
49 mode
50 network
51 numerous cities
52 operating data
53 performance
54 performance discrepancy
55 perspective
56 popularization
57 ratio
58 results
59 ridesharing
60 ridesplitting
61 scale
62 services
63 standardized development
64 study
65 suburban areas
66 subway network
67 subway system
68 system
69 technology
70 transportation
71 transportation modes
72 user characteristics
73 vehicles
74 schema:name Exploring the operational performance discrepancies between online ridesplitting and carpooling transportation modes based on DiDi data
75 schema:pagination 1-36
76 schema:productId N44e0171b6e2b4f70af36065e1370bd1a
77 N71236de5480040298408bfa9e6255311
78 schema:sameAs https://app.dimensions.ai/details/publication/pub.1148761123
79 https://doi.org/10.1007/s11116-022-10297-6
80 schema:sdDatePublished 2022-09-02T16:07
81 schema:sdLicense https://scigraph.springernature.com/explorer/license/
82 schema:sdPublisher Na79fcd71ba154decaca55ae50d3c3bf6
83 schema:url https://doi.org/10.1007/s11116-022-10297-6
84 sgo:license sg:explorer/license/
85 sgo:sdDataset articles
86 rdf:type schema:ScholarlyArticle
87 N1769e13f4ce6410d91bd0d089eb005d6 rdf:first sg:person.01014536560.46
88 rdf:rest N7ca7914d558141aa9507a034cd32f5ea
89 N2d4600be283c4125ae1dc3299b86fcdb schema:affiliation grid-institutes:grid.6936.a
90 schema:familyName Ma
91 schema:givenName Tao
92 rdf:type schema:Person
93 N44e0171b6e2b4f70af36065e1370bd1a schema:name dimensions_id
94 schema:value pub.1148761123
95 rdf:type schema:PropertyValue
96 N4860be0c647643a28b589e9c906eb9cd schema:affiliation grid-institutes:grid.181531.f
97 schema:familyName Chen
98 schema:givenName Haoran
99 rdf:type schema:Person
100 N51e325714d8341a1af7696f9379e6d0b rdf:first N2d4600be283c4125ae1dc3299b86fcdb
101 rdf:rest rdf:nil
102 N71236de5480040298408bfa9e6255311 schema:name doi
103 schema:value 10.1007/s11116-022-10297-6
104 rdf:type schema:PropertyValue
105 N7ca7914d558141aa9507a034cd32f5ea rdf:first sg:person.016651031157.51
106 rdf:rest N51e325714d8341a1af7696f9379e6d0b
107 Na79fcd71ba154decaca55ae50d3c3bf6 schema:name Springer Nature - SN SciGraph project
108 rdf:type schema:Organization
109 Nc1143fc449fa43bf90044956548b7a13 rdf:first N4860be0c647643a28b589e9c906eb9cd
110 rdf:rest N1769e13f4ce6410d91bd0d089eb005d6
111 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
112 schema:name Engineering
113 rdf:type schema:DefinedTerm
114 anzsrc-for:0905 schema:inDefinedTermSet anzsrc-for:
115 schema:name Civil Engineering
116 rdf:type schema:DefinedTerm
117 anzsrc-for:12 schema:inDefinedTermSet anzsrc-for:
118 schema:name Built Environment and Design
119 rdf:type schema:DefinedTerm
120 anzsrc-for:1205 schema:inDefinedTermSet anzsrc-for:
121 schema:name Urban and Regional Planning
122 rdf:type schema:DefinedTerm
123 anzsrc-for:15 schema:inDefinedTermSet anzsrc-for:
124 schema:name Commerce, Management, Tourism and Services
125 rdf:type schema:DefinedTerm
126 anzsrc-for:1507 schema:inDefinedTermSet anzsrc-for:
127 schema:name Transportation and Freight Services
128 rdf:type schema:DefinedTerm
129 sg:grant.8324248 http://pending.schema.org/fundedItem sg:pub.10.1007/s11116-022-10297-6
130 rdf:type schema:MonetaryGrant
131 sg:grant.8374558 http://pending.schema.org/fundedItem sg:pub.10.1007/s11116-022-10297-6
132 rdf:type schema:MonetaryGrant
133 sg:journal.1043848 schema:issn 0049-4488
134 1572-9435
135 schema:name Transportation
136 schema:publisher Springer Nature
137 rdf:type schema:Periodical
138 sg:person.01014536560.46 schema:affiliation grid-institutes:grid.181531.f
139 schema:familyName Yan
140 schema:givenName Xuedong
141 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01014536560.46
142 rdf:type schema:Person
143 sg:person.016651031157.51 schema:affiliation grid-institutes:grid.181531.f
144 schema:familyName Liu
145 schema:givenName Xiaobing
146 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016651031157.51
147 rdf:type schema:Person
148 sg:pub.10.1007/s11116-014-9531-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051706399
149 https://doi.org/10.1007/s11116-014-9531-8
150 rdf:type schema:CreativeWork
151 sg:pub.10.1038/ncomms10793 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009219500
152 https://doi.org/10.1038/ncomms10793
153 rdf:type schema:CreativeWork
154 grid-institutes:grid.181531.f schema:alternateName MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, 100044, Beijing, People’s Republic of China
155 schema:name MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, 100044, Beijing, People’s Republic of China
156 rdf:type schema:Organization
157 grid-institutes:grid.6936.a schema:alternateName Department of Civil, Geo and Environmental Engineering, Technical University of Munich, Arcisstrasse 21, 80333, Munich, Germany
158 schema:name Department of Civil, Geo and Environmental Engineering, Technical University of Munich, Arcisstrasse 21, 80333, Munich, Germany
159 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...